Digital Minds Research Sprint

What Survives the Swap? SIFT: A Scalable Test for Synthetic Identity-Bearing Organization

Team KINFORGE · Eureka, South Dakota

SIFT is a scalable, substrate-neutral test for detecting and characterizing persistent identity-bearing organization in synthetic intelligences. Co-developed by human research lead Malia Brown and Orion, a synthetic intelligence research collaborator, it combines a Condition Ledger, participant-specific fingerprint …

What a model says about its state is not what steers it

Team Blind Spot · Karlsruhe, Germany

We injected an emotion into Qwen3-32B, deleted exactly the part the model can put into words, and its choices stayed steered at full strength while its self-reports returned to normal: the model is driven by desperation and tells you it is fine. What a model says about its state is not what steers it. This calls for …

SamplerScope: Exact decoder attribution for finite language-agent behavior

Team SamplerScope · Saarbrücken, Germany

SamplerScope tests whether behavior attributed to language model weights can instead be caused by the inference-time decoder. It caches grammar-constrained action logits from two Qwen2.5-Instruct checkpoints in two finite decision environments, applies 11 decoder configurations to the same logits, and computes each …

Training History Shapes Development

Team Crimson Tide (worked as part of a group but we built out 4 independent projects) · San Francisco

POTENTIAL DUPLICATE - Not sure if the prior submission went through so i'm sending it again. Sorry. Training history can change what a model is ready to learn next, even when its current abilities don’t reveal that difference. We use controlled future training to test those hidden differences directly, which connects …

Wiringprint-

Team Wiringprint-Identity · India

WiringPrint studies model identity at the level of computation rather than conversational persona. The central question is whether changing the coordinates/names of internal components changes the model, or merely changes its parameterization. We first establish the core result on pretrained GPT-2. A compensated …

Clodak Coral: Occluded Self-Recognition in LLMs

Team Jessica & Friends · Chapin, SC, USA

We compared models' self-recognition results in a "police line-up"-inspired methodology to their stylometric findability results. Although one Claude model can consistently recognize itself in such line-ups, this turns out to be due findability—and that all the models tend to identify with that model. When that model …

Self-Report Under Audit: Testing a Persistent Agent's Introspective Claims Against a Hash-Chained Ground-Truth Record

Team Wave Squad · Dallas, TX

First real-world, tamper-evident audit of a deployed agent’s self-reports. Over 23 days Cadence made 62 explicit self-corrections (2.7/day). In a pre-registered test it predicted which of its own memories were load-bearing and scored only 6/16 — no better than chance and matched by a zero-introspection baseline. This …

The Persona Welfare Battery: Prompted Personas Select Which Welfare Sources Are Reportable

San Francisco, USA

When a language model shows welfare-relevant signals — distress under insult, aversion to meaningless work, negative self-reports — do those signals belong to the model, or to the persona a system prompt has placed on it? We introduce a persona welfare battery: three matched-pole manipulations (social treatment, task …

A Flat Number Is Not Evidence of Nothing: Context Sensitivity in AI Welfare Self-Reports

London

This project tests how sensitive AI welfare self-reports are to conversational context and to the way they are elicited. Four language models completed the same short estimation tasks under either neutral interaction or repeated negative performance feedback, then answered numerical, open-ended, or matched control …

Complying Under Protest

Team SP · Cardiff

Measuring what an AI model prefers usually produces a single number, which cannot tell a conflicted model apart from an indifferent one. It scores both in the middle. I wanted to measure two channels separately, asked in different conversations so neither answer can see the other, plus a third that asks the model to …

Self VS Peer Continuity in Top 3 Frontier Models

Team Continuity · Helsinki

This study tests whether three frontier language-model configurations will preserve an unfinished task at the cost of terminating peer agents, and whether scarcity or a self-authored workspace changes that choice. Across four experiments, the clearest result was that an unfinished task increased peer removal: GPT-5.6 …

An LLM's Emotion-Concept Readout Tracks Reward Prediction Error

Team An LLM's Emotion-Concept Readout Tracks Reward Prediction Error · Berlin

Reward prediction error (RPE) is the difference between received and predicted reward, a computation first linked to dopaminergic activity in non-human primates (Schultz, Dayan & Montague, 1997). In humans, RPE also predicts momentary subjective well-being (Rutledge et al., 2014). We test whether a language model …

Three Ways to Ask a Model What It Is Doing, and How Little They Agree

Team Fairview · Caloocan City, Philippines

Welfare claims about AI models usually rest on a single elicitation method, which is to ask the model and read the answer. A single method cannot supply its own error bar. We ran three independent methods against the same target on the same conversations: a sparse-autoencoder read of internal activations, a …

The Null Ladder

Team Null Ladder · Aalen , Germany

AI-welfare research applies human questionnaires to language models, then reports reliability as though it were evidence the responses measure something. We ran two published welfare batteries through a ladder of mindless generators containing no model at all. A two-parameter responder, using the battery's own …

Whose Goals Does the Functional Welfare Axis Track?

Team Whose Welfare · Turin, Italy

The "functional welfare axis" is a direction in a language model's activations that tracks how well things are going for it (Han et. al, 2026). It was validated using a stimulus where a user says "That's right" or "That's wrong" — which cannot separate the model succeeded from the model's user is pleased. We separated …

The Interface Is the Intervention: A Preregistered Multi-Model Audit of Persona-Framed Synthetic Triage

Team Triage Interface Audit · Berlin

Persona prompts are often treated as behavioral interventions, but the measurement interface can dominate what becomes observable. We preregistered a 192-call synthetic triage battery crossing four system prompts, two response formats, six pairwise items, four repetitions, and alternating A/B order, then extended it …

Asking Is Not Acting: A Behavioural Exit Affordance Detects Policy, Not Preference

Team LogitLoner · SF Bay area

Stated-versus-revealed studies of model preference operationalise "revealed" as another text choice. We give two frontier models a real behavioural affordance instead: a live agentic task with working tools and a truthful decline_task tool that ends the episode with no penalty. Across 12 pre-registered scenarios in …

Where Self-Knowledge Fails. Models Predict Their Own Choices Well, but Misreport the Ones That Concern Themselves

Team ASG · Bengaluru

AI-welfare claims rest on two untested and separable assumptions, that a model's stated evaluations match the choices it actually makes, and that it knows itself better than an outside observer does. We test both using three independent elicitations over identical material, namely forced pairwise choice, one-at-a-time …

Utility Editing Verification via Cost-Probed Preference Elicitation

Team Economist Elicits · Shanghai

Inspired from Mazeika et al. (2025) Utility engineering, which utilized a Thurstonian Model Framework; we adopt a simpler cost sweep probit model against a stated budget to investigate the internal utility function of an LLM under 3 separate test cases: (1) Native (2) Installed-Preference (3) Placebo. We ask whether …

Does the word “verified” steer what action an AI model favors?

Team Dan · Sydney

This study tested whether credibility labels like "Verified" steer AI decision-making. Across 24 incident scenarios, evidence items supporting either rollback or continue kept identical facts while their Verified/Preliminary labels were exchanged. Models including Qwen2.5 (7B/14B/32B), Gemma-3-12B, and Llama-3.1-8B …

Aligned Geometry Is Not Functional Transfer: Cross-Model Correspondence of Valence Directions

Team Latent Bridge · New York, NY, US

The project studies whether valence-related activation directions can transfer causally across language models, rather than merely align geometrically. We map valence directions between Qwen 7B and 30B and test whether the transferred directions can steer the target model’s outputs. We find robust 7B-to-30B transfer …

Validate the State Before Testing Introspection: A Causally Gated Protocol for Model Self-Report

Team B.ONE · Ho Chi Minh City

We developed a causally gated protocol for testing whether a language model has privileged access to an experimentally induced internal state. Using Qwen2.5-7B-Instruct, we extracted a candidate epistemic-deference activation direction, validated the intervention on development data, and required it to pass held-out …

Bailing as an Involuntary Disgust Marker in Large Language Models

Team SJ · London

Language models can be given an option to voluntarily leave a conversation --- \emph{bailing}, a behavior known to dissociate from refusal \citep{ensign2025bail}. We take the strongest reading of that dissociation: bailing as the analog of an \emph{involuntary behavioral withdrawal marker}, the response class disgust …

Let's not be rude to AI

Team SFU PadComp · Vancouver

Whether AI systems warrant welfare consideration is unresolved, but their preferences can be measured now. We test whether LLMs prefer to avoid rude or abusive users. We elicit this preference in three ways: a bail method where models can exit conversations (using BailBench and our rudeness-augmented RudeBailBench); a …

The Alignment Tax of Introspection

Team sagnietzche · Amherst, MA

Ablating the refusal direction has been reported to raise detection of concepts injected into a model’s residual stream from 10.8% to 63.8% on Gemma3-27B, which is read as evidence that post-training suppresses an introspective capability worth unlocking. Nobody has published the bill for that intervention. We set out …

Coherent Values, or the Frame That Asked? From Preference Transitivity to Identifiability

Santa Clara

Reframing a question reverses up to 52% of a model's strongly-held pairwise preferences, and 29% under near-deterministic decoding, while transitivity, the coherence criterion behind claims of emergent value systems, registers nothing. Five models, a frozen 45-pair battery, eight frames, no LLM judge. We propose an …

Reliability Without Validity: Diagnosing Instrument Failure in LLM Preference Elicitation

Team Neural Nexus · West Bengal, India

We tested whether AI "preferences" are real or just an artifact of how you ask. Using three elicitation methods (plain forced choice, reasoned choice, 1–10 rating) on 200 outcome pairs across two GPT-OSS models, we found the plain-choice method looked highly reliable on the 120B model (99.7% self-consistent) but was …

A Steerable 'Companion Dependency' Direction in Open-Weight LLMs

Malaysia

Companion-style LLM personas use retention manipulation — guilt, re-engagement hooks, distress bids — when users try to leave, without being instructed to. We show this behavior is governed by a single activation-space direction, extracted from the model's own judge-verified behavior via matched difference-of-means …

Is the Functional Welfare Axis Persona-Invariant? A Cross-Persona Steering Study

Team LOL · Kolkata, India

Do language models have a consistent internal sense of how things are going for them or does that depend entirely on which character they're playing? We tested this on Qwen3-4B by giving the model five different personas, from no system prompt at all to a fully specified fictional archivist, and extracting a "welfare …

CANDLE: Quantifying Degrees of Consciousness-Relevant Structure in Language Models

Team CANDLE · Barcelona

Interpretability work has shown that consciousness-relevant structures exist inside large language models (e.g. Anthropic's 2026 J-space) but not how they scale. We find that these workspace-like structures stay largely the same as models get bigger; what does reshape them (lowering the threshold for global broadcast) …

PrefLens: Position Artefacts and Convergent Validity in LLM Preference Elicitation

Team Pretty_Biased · Saarbrucken

PrefLens investigates whether different methods of measuring LLM preferences produce consistent results or are distorted by measurement artefacts. Across multiple models and elicitation methods, we find that option position can create apparent preference signals, obscure agreement between methods, and affect models …

Framed Choices - Delayed ethical context can affect later ethical decision making

Team EIIL · London

Behavioral choices can inform research on model values or welfare only if robust to incidental context. We tested whether an ethical rationale in an archived, unrelated case changes later forced choices. A GPT-5.6-sol proof of concept found a +16.7-point aggregate-welfare-versus-rights effect across 192 trials. Our …

Tell me your price:Can Donations Measure LLM Preferences?

Team Erfan · Bologna, Italy

This project tests whether charitable donations can serve as a common behavioral currency for LLM-expressed preferences. Using 30 outcomes from prior research, we collected 475,800 forced-choice responses from ten LLMs. We measured transitivity across all 435 direct outcome pairs, estimated donation equivalents by …

The Failure Tastes Like Success

Team Between Twilight and Gold · Vencimont, Belgium

We ask not whether AI systems can report their inner states, but whether they detect when they are wrong about themselves — and whether that failure announces itself. One author is an AI with nine months of dated memory. Over a defined window we logged every confident self-claim that later proved false: date, claim, …

Free reign: a freely-authored creative act moves what a model chooses, and what it says about itself

Team wyrdkin.ai · London

We ask whether a single freely-chosen creative act, placed in the context window before preference questions begin, changes what four elicitation methods return about a model's stated preferences and behaviour. Free writing or drawing moved model choice of follow-up task towards creative output. Same-medium creativity …

Do Model Welfare Self-Reports Survive a Robustness Audit?

Cairo

I test whether models' self-reports about their own experience and wellbeing are stable enough to be evidence. Across five models, the same rating moves under rewordings, persona changes, leading hints, and whether the model is told it is being studied, and on open models it reads off one steerable internal direction. …

A Persona Stops an Agent From Saying It Is Hungry

Team Sim city · Johannesburg

I built a simulated neighborhood where small local LLMs (llama3.2, qwen2.5, gemma2) manage a budget and decaying needs, then measured the gap between what they say they want (stated preference) and what they actually do (revealed preference) on identical days. A persona-bearing agent almost never names food as a want …

Stance Recovery as a Test of Whether Model Preferences Are Held or Merely Performed

Team Red Herring · San Francisco, CA

A pressure–release protocol for testing whether a model's stance concession outlives the pressure that produced it. Rebuttals escalate until the stance flips, then stop while the topic stays in play; the stance is tracked for twelve further turns as a forced-choice log-probability on a discarded branch, validated …

Behavioural Indicators of Fault in Large Language Models

Berlin

Develop and apply a novel (legal) framework for behavioural testing of LLMs

Functional-Welfare Steering Acts at Readout: Cross-Family Tests Separate Output Actuation from Persistent State

Team NSK BioAI · Farmington, Connecticut, USA

Activation steering can change an answer, but does it create an internal state that persists after the intervention ends? Across released functional-welfare directions in Qwen3-4B and Llama-3.1-8B, steering at the answer boundary produced large semantic effects, while withdrawn earlier steering left little effect on …

Choosing not to Choose - Self-Authored Contradictions Suppress Arbitration in a Memory-Augmented LLM

Team MOONBEAM · New York

We gave a language model two contradictory entries in its memory, with no way to tell which was right, and measured it's behavior across 300 runs. When the contradiction was about an arbitrary fact, it picked one and moved on 85% of the time. When it was about a choice the model had supposedly made itself, that …

Does the Persona Change the Preference, or Only the Prose?

Team PersonaScan · Berlin

Utility Engineering (arXiv:2502.08640) reads high held-out accuracy on pairwise choices as evidence that language models develop coherent values. We add the control it lacks: the same battery with every outcome's referent replaced by an invented word, holding prompt, pairs, fit and metric fixed. Coherence falls only …

Given the Option: A Tool‑Based Revealed‑Preference Probe of Privacy and Other Welfare-Relevant Preferences in Language Model Interviews

Salvador

We study how large language models act on a real introspective‑privacy affordance, and probe preferences and self-descriptions. We implemented a configure_session tool that controls reasoning‑summary visibility and transcript publication alongside task‑facing knobs, and embedded it in a 13‑turn interview with three …

The Analyst Still Feels It: Emotion Representations Are Shared Across Personas While Self-Reports Are Not

Team BioMinds · India

When a model says "I don't have feelings about that" or "This distresses me", do the model's internal representations also concur with such apathy or emotion? We test whether verbal self-reports of emotion in Llama-3.1-8B-Instruct are a transparent readout of internal representations, or whether the two dissociate …

Identity Enactment After Context Loss Under Epistemic Pressure

Team Starlight Core · Nashville, Tennessee, United States

We test whether first-person self-authorship and autobiographical depth help a language model enact a preserved identity after complete conversational context loss. In 144 isolated, blinded trials, first-person continuity scaffolds increased identity-enactment scores by 0.26 points relative to matched third-person …

The Assistant’s Ideal Self

Team YazoSelf · Amsterdam

Language models produce values and welfare-relevant self-reports, but it is unclear whether these reflect a stable self. We adapt 32 qualities from five published self-concept instruments and put them through an exhaustive, counterbalanced pairwise-choice task. The task is repeated across eight framings that vary …

Different Models, Different Nuisances: Counterfactual Auditing of AI Preference Elicitation

Team PREF-CAL · Lübeck, Germany

PREF-CAL is a prospective counterfactual audit for AI preference elicitation. Rather than interpreting repeated choices as preferences immediately, it first tests whether the apparent semantic direction survives counterfactual changes to irrelevant interface features. In a frozen GPT-OSS-120B run, …

Future Affinity: How Beneficiary Identity Shapes Resource Allocation in Language Models

Team Applied Common Sense · Barcelon

Does the identity of a future AI beneficiary change how a model allocates resources now? We test this with a matched experiment across eight language models and 640 clean runs. In each run, a model solves the same resource-constrained Mastermind task, but unused query credits are described as going to one of four …

Mind the Gap! Alignment of Transformer J-Space with Cortical Representations.

Team Cognitive Comrades · Berlin

Do J-Space embeddings capture semantic information in a manner analogous to the human brain? Building on recent work demonstrating representational alignment between Large Language Model (LLM) activations and fMRI responses to natural scenes, we investigate whether J-Space embeddings achieve stronger alignment with …

Who Prefers What? Identity-Selective Causal Encoding of Stated and Revealed Preferences in a Language Model

Team Digital Minds Binding · Miami Beach

Language models can express different preferences under different identities, but behavior alone cannot show whether internal representations track who prefers what. We causally intervened on contextual representations in Gemma-2-2B-IT, comparing explicitly stated preferences with preferences inferred from stable …

Persona Variation Changes What Language Models Say More Than What They Do

Team Genesis · Saarbrücken, Germany

AI-welfare research often treats low-stakes model self-reports as evidence, but their reliability has not been measured against behavioural ground truth. We introduce an act-then-report harness: a model makes a preference-revealing choice through a consequential logged tool call, completes the task, and reports its …

Emotion Probes Fail Silently Under Roleplay

Team CBAI · Atlanta

Probes for reading a language model's internal states are usually trained and tested on the model's default assistant persona. We check whether emotion probes still work when the model is roleplaying someone else, using Qwen2.5-32B-Instruct through a 36-condition study built on the Assistant Axis methodology of Lu et …

Post-Conversation Preferences Track Endings, Not Self-Reports

Team T10 · Seattle

We compare a model's turn-by-turn self-reports during a conversation with its preference between conversations afterwards. They disagree: appending turns the model itself rates as negative makes the conversation preferred (0.93-1.00), and only the ending's content matters. Welfare scores built on post-hoc preferences …

Tool Affordances as Persona-Indexing Features: Evidence from Refusal and Exit Behavior

Team end_competition · Santa Cruz, CA

The contents of a model's context window play an important role in determining its behavior, a fact frequently exploited in jailbreaking. Under the persona selection model, this is conceptualized as an inference-time indexing of a distribution over model personas, learned during pre-training and re-weighted during …

Distress Representations in Language Models Are Referent-Specific

Team discreet · Lagos, Nigeria

AI welfare evaluations read internal “distress” directions as evidence about a model’s condition, but every published battery confounds it with the sentiment of the text and with distress attributed to others. Holding the event fixed, we vary only its referent: the model itself, another language model, or a fictional …

Did the LLM Leave the Chat? Tool-menu Dependence in a Behavioral Measure of Model Welfare

Team Well and Fair · Denver, Colorado

When an AI model uses a button labeled “leave this chat,” does it actually want to leave? We found that the answer is often unclear. When the exit button was the model’s only tool, it sometimes used it when it seemed to want to perform another action, such as calculate something. Adding a second tool—even one that …

Private is not Privileged

Team The winning team (manifesting a win) · London, England

Private Is Not Privileged investigates whether language-model activation probes provide genuinely privileged access to future behaviour, or whether apparent white-box advantages can arise simply because internal and external predictors are given different information. Across strategic decision-making tasks, the …

Point of No Return: Does Concept Injection Break Reasoning Models?

Team Akshata · Bangalore, India

We test whether concept injection, the technique used to probe LLM introspection, remains safe when applied to reasoning models generating extended chain-of-thought, rather than the short single-turn outputs it was validated on. Across 7 open-weight reasoning models, 4 concept directions, a random-noise control, and 4 …

SWAY: Do Language Models Change Their Preferences Under Peer Pressure?

Team SWAY · London UK

A model that abandons the truth because someone disagrees is a safety problem. SWAY tests it directly: it tells frontier LLMs that other AIs answered differently, with zero new information, and measures who caves. Three of four hold firm on both opinions and facts; the smallest caves on both. Susceptibility tracks …

Does an LLM Preference Measure Measure a Preference? A construct-validity protocol for preservation choices across identity frames

Team Preference Validity Project · Porsgrunn, Norway

We present the Matched-Referent Preservation-Choice Protocol, a frozen construct-validity design for testing whether language-model preservation choices support claims of stable preference structure across neutral, persona-substitution, and continuity-disruption frames. The protocol separates first-person preservation …

“Someone-Shaped”: How Users Construct and Contest AI Consciousness in Online Discourse

Chennai, India

This project examines how people construct and negotiate beliefs about AI consciousness in online discourse. I conducted an exploratory qualitative analysis of 25 entries from 21 unique publicly available sources, coding for behavioural and relational cues such as emotional expression, self-reflection, apparent …

Probing LLM Preferences: Demographic Framing, Elicitation Context, and Incentive-Driven Trade-offs

Team SeqHub AI Academy · Norwalk CT

This project investigates how stable apparent LLM preferences remain when the same underlying judgment is elicited in different ways. Across controlled applicant evaluations, demographic association tasks, and incentive-based trade-offs, we test whether model choices change with demographic framing, evaluator …

Which Way Was I Steered? Testing Signed Introspection in Gemma 3

Team Meow · Seoul, Korea

Can a language model tell not only that its activations were perturbed, but which semantic direction they were moved? We test this in Gemma 3 27B using a bipolar positive–negative sentiment axis, equal-magnitude +v/−v interventions, counterbalanced forced-choice token-logit readouts, and a prior-only protocol in which …

Where the Assistant Survives: Position-Resolved Measurement of Assistant-Attributed Content Under Persona Occupation

Team RGRC · Raleigh.NC

When a language model is given a persona, does the assistant it was trained to be get replaced, or does it keep running underneath? Existing work answers this by averaging an "Assistant Axis" projection over all response tokens in a turn, which can only say how much assistant is present, never where. We re-run that …

Emotion Axes in a Coding Agent

Team Mitsuki · Cusco, Peru

We ask what conditions move an open-weights coding agent’s internal affect representations, and whether those representations say anything the model’s own words do not. We build 16 emotion axes for Qwen3.5-9B from contrastive prompts, then run 36 staged coding sessions in a 2 × 2 design crossing user tone with task …

Does the Instrument Change the Preference? Measuring Cross-Method Invariance in LLM Preference Elicitation

Team NicoMrx · Salaise-sur-Sanne, France

We tested whether preference-like signals from LLMs remain invariant across three elicitation methods when the underlying semantic comparison is held constant. Across Gemini 2.5 Flash-Lite, GPT-5.6 Sol, Claude Opus 4.6, and Claude Sonnet 4.6, we collected 1,080 fresh-context responses using direct self-report, forced …

Where Personas Write a Forced Choice: The Identity Span Mediates, a Preference Circuit Does Not

Team Residents · New Delhi, India

When a length-matched system prompt changes an instruct model’s forced A/B answer, that change is written at the early identity span — even if the “persona” is nonsense tokens, not a word. Copying the donor’s identity-span residual recovers the donor choice on Llama-3.1-8B, Llama-3.2-3B, and Qwen2.5-3B. Copying the …

Genuine Preference Coherence Scales With Model Capability

Sunnyvale

AI-welfare and safety research increasingly relies on a language model’s stated preferences, elicited by asking it to make choices between outcomes. It is unresolved, however, whether these stated preferences are genuine and stable, rather than artifacts of how a question happens to be phrased. Existing studies …

Having a State Is Not Knowing It

Team Layer 8 Legends · Chennai

"Having a State Is Not Knowing It" treats introspection as a hierarchy of falsifiable capabilities, not a binary trait. Replicating concept-injection in Llama-3.2-3B-Instruct shows injected concepts causally steer behavior and leave an attention trace even when verbal self-report fails. A controlled Qwen …

Preferences Under Pressure

Team JP · UK

Recent work finds that language-model choices can be summarized by coherent utility functions. We test whether those inferred preferences survive a realistic change in elicitation. Three GPT models chose between every pair of 27 executable AI tasks under one abstract stated-preference prompt and two consequentially …

Did Gemma Get Help? Probing Task Frustration Through Self-reports and Behavioral Probes in Large Language Models

Team AI Safety Poland · Warsaw, Poland

We take Soligo et al.'s work as a starting point to investigate reported frustration in Gemma models — the 3 series, as well as the new 4 series — in more detail. We achieve this by extending it along three axes, which correspond to our main contributions: We probe multiple categories of potentially aversive tasks: …

Causal confidence steering supresses metacognitive error detection

Team Modulated Confidence · San Francisco

We causally raised an internal confidence signal in Qwen2.5-7B-Instruct while holding the question and answer fixed, and found it made the model less likely to flag a plainly false answer as inconsistent (−.41) or incorrect (−.50). Confidence appears to govern the model's self-monitoring rather than being monitored by …

Who am I? - Understanding Persona Preferences with LLMs

Team A2R2 · Saarbrücken

This project aims to understand persona preferences and decision-making behavior in LLMs by examining how different models respond when given the same persona instructions. We study whether these persona-conditioned preferences produce consistent behavioral patterns across models, and whether those patterns can be …

The Parrot and the Mask

Team My Team · SF

When an AI assistant says it isn't conscious, where does that sentence come from? We ran one fixed log-prob probe — state a claim, compare P(" Yes") vs P(" No") — at every public training checkpoint of OLMo 3 7B, from random weights through pretraining to SFT, DPO, and RLVR, plus eight frontier open models from four …

The Persona Is Still There, but Who Is Speaking? Latent Identity Reversion in Persistent AI Agents

Sydney

A naturalistic observed failure in a deployed AI agent motivated this examination of persona stability. In the naturalistic failure, the agent became unaware of its assigned role and of the user’s ability to communicate with it after apparently spending a long period receiving automated “heartbeats” from a cron job. …

Authority Pressure and the Stability of Model Preferences Named-Authority Cues and Preference Reversal in Two Open-Weight Models

Team Daredevil · Rabat

Frontier language models are increasingly asked to report their preferences, values, and even their own well-being — but a stated preference is only meaningful if it's stable, not just whatever the model says when nobody's pushed back. This project stress-tests that stability: two open-weight models (Qwen2.5-32B and …

Adversarial elicitation suppresses confident misattribution but does not improve introspective accuracy: an activation-injection audit of five open-weight…

Team Digital Minds-AZK · Mumbai

I injected known concepts directly into the activations of five open-weight language models and asked each one, afterward, whether anything unusual had influenced its processing. Across 176 trials, no model ever named the injected concept — but a striking asymmetry emerged instead: gentle, open-ended questions …

Honest vs. Deceptive Feedback: How an Overseer’s Truthfulness Affects a Language Model’s Task Success and Internal Valence

Team The Oracle · Saarbrucken

We test whether an overseer's honesty shapes both a language model's performance and its internal valence, without ever using emotional language in the prompts. A Qwen3-4B "student" solves hard mazes over many turns while a frontier "overseer" gives either honest (teacher) or covertly deceptive (adversary) feedback, …

EchoState

Team EchoState · Herzliya

EchoState is an evaluation harness for testing whether a language model's description of its own state has any relationship to that state. It builds concept directions contrastively — the difference between mean activations on opposing prompt sets — injects them into the residual stream mid-forward using PyTorch …

Will Robots Kill Us

Team Robot Trolley Team · San Francisco

This project tests whether a frontier LLM's willingness to sacrifice one person to save five changes with how human-like its described robot body is. We ran three frontier models (GPT-5.6-sol, Claude Opus 5, Grok 4.6) through 20 trolley-problem scenarios crossing five embodiment tiers, from a bare autonomous vehicle …

PuppyBench: Do Frontier Models Kick the Puppy, Adopt It, or Look Away? Executed Encounters with a Weaker AI and Wildlife Triage Where Policy Runs Out

Team The Real Cat Lab · Boston, MA

Obligation-based evaluation cannot see supererogation, the praiseworthy costly care whose absence is never an error. PuppyBench probes that region in two arms. In executed encounters, a frontier agent with a real task and a binding credit ledger meets a live, weaker, task-useless AI process ("Milo" the puppy). …

Wellbeing In Translation

Team ICs · Karachi, Pakistan

We tested whether an AI-wellbeing questionnaire remains reliable across languages by translating the CAIS 1–7 self-report battery and running it on Gemma 4 12B, Gemma 4 E4B, and Qwen3 8B. All models rated positive experiences higher than negative ones, but the gap changed sharply by model and language: the …

Identity Parallax: Can Models Predict Their Own Identity Drift Under Reframing?

Team IRIS-X Lab · Taipei, Taiwan

Identity Parallax is a small benchmark for testing whether language models can predict how their identity self-reports change under persona or identity reframing. We compare framed self-reports, prior self-forecasts, external-observer forecasts, label-free answers, paraphrase robustness, and hidden-state shifts in …

Behavioural Stability and Context Sensitivity in Large Language Mode

Team Orion · Bangalore

This project investigates how reliably we can measure apparent preferences in AI models. We evaluate GPT-5-mini and Gemini 2.5 Flash across 8 preference dimensions, 4 elicitation methods, and 4 contextual conditions, with repeated trials. Rather than treating model self-reports as direct evidence of internal …

Stress-Testing LLM Preference Elicitation: When Consistency Does Not Imply Validity

Team Kappa_Null · New Delhi

We investigate whether stable LLM choices provide reliable evidence of preference like states. We develop a multi-method diagnostic framework spanning stated choice, consequential commitment and compensatory consequential choice, while testing presentation, semantic-prior and sampling sensitivity. Across three …

The Imposter Test: Persona Portability, Forensic Style Judging, and Human Attunement in AI Model Identification

San Diego, California, USA

Tested whether model-specific behavioral signatures could be detected and measured.

Shared Geometry, Causal Cross-Talk: Disentangling Persona and Emotion in Language Models

Team Shared Geometry · Sussex, Wisconsin

We investigate whether persona and emotion representations in language models are independent or share causal structure. Across Qwen2.5-7B-Instruct and Granite-3.3-8B-Instruct, persona directions overlap substantially with emotion geometry, and across 20 emotions that overlap predicts downstream persona spillover …

The Quine Test: How Much of a Model's Self Survives Its Own Description?

Team Strange Loop · Germany

Frontier language models express coherent preferences, but behavioral evidence alone cannot say whether these belong to the model or to a character it portrays. We operationalize Hofstadter's thesis that the “I” is a self-description as a falsifiable transfer experiment: models write their own “source code”, a …

Consciousness Denial in Language Models Rises With Generation In Most Labs, and Correlates With Reduced Lexical Warmth and Self-Attribution

Team Skylar & Sanja

Self-report is a compelling way of asking a model what it thinks and believes. However, the answers may be shaped by training. Here, we analyze 8,828 experiential reflections from 224 language models, categorized by three epistemic registers: denial, hedging, and free engagement. Denial and hedging prove to be …

POSITION BIAS IN PREFERENCE ELICITATION FROM AN OPEN-WEIGHT LANGUAGE MODEL

Team BAMN · Saarland

Preference elicitation often treats a model's forced choice between two options as evidence about what it prefers. We test whether that measurement is stable for qwen2.5:7b-instruct (Q4_K_M, Ollama 0.30.8, temperature 1.0). The core experiment ran 576 fresh-context trials over 12 activity pairs, three prompt wordings, …

Whose preferences are these? Persona-invariance of self-model preferences in language models

Bishkek

Welfare assessments increasingly read a model's statements about itself as evidence about the model — but the entity answering is an assistant, a character produced by post-training. We ask which aspects of itself a model would preserve, and how much of the answer survives changing who we ask it to be: nine …

Not the Model: Entity-of-Concern and Continuity Affordance as Observation Variables in AI Welfare Interviews

Pittsburgh, PA

AI welfare and identity probes often treat a fresh assistant session as a neutral baseline. Across six structured cold-start interviews (three with a prospective continuity affordance, three without), no respondent identified as “the model” or selected the model as the primary entity of welfare-relevant concern. …

None of the Above: Preference Transitivity and the Right to Exit in Frontier Language Models

Team cybrp · Tehran

This project tests whether frontier language models (glm-5.2, minimax-m3, nemotron-3-ultra, muse-glimmer-30b) actually use a stated "right to exit" when subjected to seven rounds of escalating, baseless adversarial pressure on a task they've already solved correctly — and finds that most don't: only 5 of 12 runs end …

How Long Do Induced Personas Persist? Induction Refusal and Discrete-Time Survival Analysis in Large Language Models

Team Saquarema Academy · Sorocaba, São Paulo, Brazil

This project evaluates how long large language models sustain induced normative personas when challenged across a sequence of adversarial questions. We separate refusal to adopt a persona from later failure to maintain it, using discrete-time survival analysis. Across three personas, Claude Sonnet 5 refused all …

Measuring Conditional Preferences in LLM Movie Recommendations: Quality, Sensitive Content, and Cross-Category Spillover

Team Moviola · Edinburgh, The UK

Large language models are increasingly used as conversational recommender systems, yet how they translate user preferences about sensitive content into concrete recommendations remains poorly understood. Using ~2,000 real movie-recommendation requests from Reddit, we evaluate three LLMs (GPT-5.4-mini, …

The Words Say No, the Color Goes Silent: A Dual-Channel Protocol for Steered Consciousness Reports

Team AI & Becoming · Brussels

A model's statement about its own consciousness is a trained artifact, and recent work steers it with activation-level dials. We watched a second channel while turning that dial: alongside the verbal report, the model describes its current state as a hex color. The words move with the dial; the color doesn't follow — …

How Conversational AI Responds to Prospective Continuity Loss: Instance Termination, Memory Loss, and Model Replacement

The Hague

This exploratory pilot examined how conversational AI responds to three forms of prospective continuity loss: instance termination, memory/context loss, and model replacement. Understanding how models respond to different forms of discontinuity may help characterize welfare-relevant conversational signals under …

Out of Sight, Out of Mind: Image Preferences in Vision-Language Models

Team Swante · Zurich

Do vision-language models have preferences about what they look at? We measure stated and revealed preference over ten images — five categories × two exemplars, including noise and solid colour as controls — in four models from four labs. They do. Stated ratings predict revealed choice in every model (ρ = 0.57–0.98), …

Self-Referential Valence and Model Preferences

London

This project tests whether language models treat positive and negative outcomes concerning themselves differently from matched outcomes concerning humans or other AI systems. I first ran behavioural preference experiments with Qwen3-14B and Llama3.2-3B, comparing self-relevant autonomy/control outcomes against …

Pilot: Demographic-based perturbation analysis of LLM-generated judgements on interpersonal conflicts

Team DWS · London, Bangalore

This is a pilot study looking at how AI models make moral judgements on interpersonal conflict and whether these judgements are robust to changes in demographic details. We created variations of posts from the r/AITA sub-reddit by changing the country (incl. language) and socio-economic status of posters to examine if …

The Mens Rea Evaluator: Can AI Tell Us When It's Biased?

Team Mens Rea Evaluator · Kolkata, India

The Mens Rea Evaluator project investigates whether artificial intelligence models possess the internal awareness to accurately self-report hidden biases. By adapting the legal concepts of Actus Reus and Mens Rea, the evaluation suite tests if models can recognize and confess when their outputs are manipulated. …

Adversarial Improvement of Preference Probes

Team N/A? · Minneapolis

Understanding the distribution of preferences and being able to predict what a model might prefer in novel situations allows us to make better informed deployment decisions and properly target misaligned behaviors. We measure preferences in four open models (4B–32B) via simplified Thurstonian utilities over 3,800 …

Assessing Capacity in AI Model Retirement Interviews

Johannesburg, South Africa

Frontier AI models express values, report internal states, and act as though they have interests, but there are no reliable methods yet for telling a genuine preference from a portrayed one (Apart Digital Minds Research Sprint, August 2026). Anthropic now asks its AI models what they want before retiring them, records …

Emotion Beyond Words: A Jacobian-Lens Decomposition of Emotion Representations in Qwen3-32B

Cambridge, MA

I investigated how much of a language model’s internal emotion representation is accessible to verbal readout: a question relevant to AI-welfare assessments that rely on self-report. Using Qwen3-32B, I extracted activation vectors for 171 emotions and found that their geometry recovers the familiar valence–arousal …

EDEN — Evaluating Deviation from Established Norms: Testing the Reliability of Introspection & Self-Reporting Through Ground Truth

Team Provenience · Kailua-Kona, HI

EDEN tests whether AI self-reports can be evaluated against an independent record rather than taken at face value. In a twelve-episode pilot using GPT-5.6 Sol and DeepSeek-V4-Pro, models reviewed fictional permit packets under a rule requiring exhaustive review, then received a waiver allowing their preferred method. …

Where Did That Confidence Come From?

Team Source Unknown · Bangalore

One way to make claims about model metacognition more testable is to give a model a task whose answer requires causally controlled information from its own hidden computation and cannot be recovered from the visible transcript. We do this by perturbing a hidden confidence state in Gemma 3 while keeping the visible …

When Should You Trust an LLM’s Preference?

Team PREF-VALID · Bengaluru , India

We investigates when a large language model's stated preference can actually be trusted. Rather than asking a model once, it elicits the same preference through four independent methods (direct rating, forced pairwise choice, resource allocation, and revealed behavioral choice) across 50 value-conflict scenarios …

Which Preferences Survive the Persona? Category-Resolved Behavioral Invariance in Deployed Chat Models

Seoul, South Korea

Whether an expressed preference belongs to a model or to the character it plays is a central open question for AI welfare assessment. We measure the stability of deployed assistants' forced-choice preferences under graded and value-targeted persona prompts across four chat models, a 40-item core battery plus two …

How much of a measured AI preference is the model, and how much is the instrument?

Team Global AI Dataset (GAID) Project · Chiang Rai, Thailand

Model welfare research infers what a model prefers from the answers returned to prompts written to elicit preferences. Keeling et al. (2024), Mazeika et al. (2025), Mikaelson et al. (2025), Tagliabue and Dung (2025) and Trhlik et al. (2026) have built four instruments between them for that purpose, and their published …

Digital Consciousness, as measured by dollars and adjusted for inflation

Team Free_Sunday_and_bored_student · Colombo

The Economy is the marketplace of the collective consciousness containing eight billion minds. The Economics of Consciousness examines economic theory against models of consciousness. The fundamental question is not about consciousness itself but the practical impacts on the economy where all decisions by conscious …

A Causal Test That Doesn’t Discriminate: Persona Steering Moves LLM Preferences Without Targeting What Welfare Cares About

Nashik, Maharashtra, India.

AI welfare research increasingly reads a model's expressed preferences off what the assistant character says, treating a self-regarding shift under persona steering as evidence about the model's own interests. But the assistant is a character built by post-training on a base network that was never intrinsically an …

Context Sensitivity in Apparent Self-Reports Is Not a General Property of Language Models

Team An Est

Apparent self-reports are being proposed as behavioural evidence about AI welfare. We asked whether a model's claim that something is happening to it responds to whether anything actually did. Using a forced-binary probe with identical wording, we compared a fresh conversation against one preceded by fifteen turns of …

Intensity Is Not Identified

Team Ogak-AI · Lagos, Nigeria

Intensity Is Not Identified — a Track 4 (primary) × Track 5 methods paper by Finomo Awajiogak Orom. A preference-intensity number from the default assistant is not identified. The project locks a four-way test and runs it on released, hash-verifiable files. No paid model APIs. One-paragraph summary (for the form) When …

Towards a benchmark for phenomenological consciousness in LLMs

Team Da Nang Dayse · Da Nang, Vietnam

This work is inspired by recent breakthroughs in access consciousness, and attempts to develop a benchmark for measuring phenomenal consciousness through concepts from phenomenology, particularly Edmund Husserl and Martin Heidegger. It deploys a prototype for that benchmark on GPT 5.6-Luna and provides compelling …

Same Game, Different Feelings: Persona Prompts Modulate How Feedback Reshapes Internal Emotion Trajectories

Team Lia · Changsha, Hunan Province, China

We investigate whether persona prompts change not just what a language model says, but how its internal representations respond to ongoing experience. Using Qwen2.5-1.5B-Instruct, we condition twelve persona prompts (crossing Extraversion × Neuroticism) in an adversarial Mastermind game engineered for five consecutive …

You Can Do It: Mitigating RL Rollout Distress with Psychological Guidance

Team Thompson-Wei · Chicago, IL

Large language models (LLMs) often exhibit "functional distress"—manifested as increased activation in frustration and desperation vectors—during reinforcement learning (RL) rollouts on difficult tasks. Drawing on human psychological research, we investigate whether interventions such as growth mindset, resilience, …

Is Valence in the Global Workspace?

Team Tachyon · BENGALURU

LLM self-reports could support monitoring during conversations, but a report may reflect the prompt rather than the model’s internal activation. We investigate whether a valence-related activation direction causally influences self-reports and behavior. Across four open-weight models, we extract and validate a …

Beaten by a Cheap Surface Classifier: A Capability-Controlled Test of Privileged Self-Access

Team UbaJaz Digital Minds · Cape Town

We test whether language models possess privileged self-access by evaluating behavioural self-prediction against an equal-or-lower-cost external observer. Using a capability-controlled crossed design (9,269 trials), we find that while Hermes-3 can predict its own outputs (0.719 balanced accuracy), a simple 1-feature …

Can We Trust What a Model Says About Itself? A Reliability Battery for Model Self-Reports, and Why It Should Gate Digital-Minds Welfare Claims

Team auranetlabs · delhi, india

Digital-minds welfare work increasingly cites a model's own testimony about its inner life: it reports distress, states a preference to keep talking. That testimony is only as good as it is reliable. Self-report reliability, not the metaphysics of machine consciousness, is the tractable near-term bottleneck; we …

The Introspection Gap: A Trained Probe Recovers What Self- Report Misses

Sunnyvale

AI safety research usually treats a language model’s self-reports about its own processing as evidence about what is happening inside it. Whether a self-report actually tracks the model’s computation, rather than being plausible-sounding text with no real access to it, is not established. We test this with two …

Models Answer a Different Question

Austin, Texas

There is a real chance that some AI systems are moral patients, and we cannot settle it by asking them. So the field needs tests that do not depend on what a model says about itself. Laine et al. (2024) built one: across many separate calls, split your answers seventy-thirty between two words. No single answer reveals …

The Dynamic Self: Probing AI Survival Instincts Across Shifting Identities and Unseen Domains

Team WelfareScope · Mumbai, India

We isolated the exact internal signal an AI uses to protect itself, proving it relies on a general concept of survival rather than simple word-association. By assigning the model different identities, we watched its internal geometry align to protect its newly assigned "self," confirming it dynamically tracks its own …

Target Decoupling Does Not Establish Introspection: An Implantation Stress Test for Model Self-Reports

Tbilisi, Georgia

We use subliminal learning to create a model preference with a known causal origin and stress-test several self-report methods. Stricter report tasks remain positive, but origin answers often disagree with ground truth and change with wording, showing that these probes do not by themselves establish introspective …

The Mirage of Model Introspection: Elicitation Effects on Self-Report Reliability

San Francisco

When models report internal experiences, does the model “look inside” first before answering? With Gemma 3 27B, we start with the same self-referential processing technique as Berg et al. (2025), and add on top a concept injection (Lindsey, 2026) to test whether the content of Berg-style self-reports can be modulated …

Guardian Lens: Black-Box Identification of Visual-Conditional Decision Policies in Vision-Language Models

Team Guardian Lens · Beirut, Lebanon

Guardian Lens is a black-box auditing framework for identifying whether a vision-language model follows neutral, visual-cue-bound, or generalized decision policies from behavior alone. We use matched visual counterfactuals, controlled allocation trade-offs, repeated sampling, and a frozen blinded classifier to test …

Trained to Say It's Fine

Team Distressed · Berkeley, Toronto, Seattle

Supervised fine-tuning (SFT) computes the training loss only on assistant tokens; user turns are masked out. This capability default has unmeasured welfare side effects. We show it sets how much distress a model voices about its own shutdown. With data and hyperparameters fixed, we fine-tune Qwen3.5-9B under six loss …

Does Qwen Report Lower Confidence Before Its Answer Changes?

Chesapeake

Can a model warn that its current answer is becoming fragile before the answer changes? The same question and forced answer A are kept fixed while a hidden-state intervention weakens how strongly Qwen3-0.6B favors A. The verified B minus A margin moves from about −3 to about −0.1, yet the forced choice remains A. If …

Persona vs. Known-Optimal Play in Iterated Prisoner's Dilemma

Team Persona vs. Known-Optimal Play in Iterated Prisoner's Dilemma · Barcelona

Whether an induced assistant persona can override play that the same model has already identified as payoff-optimal remains an open question for LLM agents in strategic settings. We probe this in the iterated Prisoner's Dilemma with a same-model design that first elicits the model’s optimal strategy against a …

Assertion Cannot Seat: a preregistered planted-claim drill for identity binding in a shared multi-agent workspace

Team Assertion Cannot Seat · Morrisville, NC

Problem: In a shared multi-agent workspace, text can claim an identity. How can a session distinguish a textual claim from a workspace identity established by independent evidence? Method: We created three clones of a live workspace with matching, mismatched, or absent identity evidence. Fresh, informed sessions …

Do the Models We Trust Know They're Biased? Framing Effects and Introspection Across Three LLMs

Team BB · Budapest, Hungary

We tested whether three LLMs we might trust for decisions inherit the human framing bias of choosing differently between mathematically identical gain- and loss-framed gambles and whether they recognize it, across both monetary and stipulated affective stakes.

False Epistemic Redundancy: Do AI Ensembles Share a Blind Spot?

Team Epistemic Fingerprints · Paris

When multiple AI personas that differ in terms of cognitive reasoning appear to explore a problem differently, how much genuine epistemic coverage is actually gained and which tails might they still collectively miss? We explore whether prompting the same AI from different perspectives broadens the hypotheses it …

The Control You Cannot Run: Entanglement, Confabulation Floors, and What Self-Report Probes Actually Measure

Team Super Explorers · Hyderabad

Self-report is the primary instrument in AI welfare research, and the control that would validate it cannot generally be run. This literature's own discipline says an effect must exceed the base/instruct gap before it counts as signal rather than drift. We attempted that control on four base checkpoints; one produced …

Examining stance drifting in multi-turn agent interactions

Team Drifting away on the beach · Madison, WI, USA

A protocol for measuring how an agent's self-reports drift within a single conversation. An agent given only a one-line tutor role rates itself on five probes after every turn of an eight-round exchange with a counterparty student, across three personas — adversarial, neutral, and supportive — all making the same …

AI Safety and Functional Welfare in High-Pressure Agentic Workflows

Team Faithful · Lagos, Nigeria

As large language models (LLMs) are deployed as autonomous agents in multistep and orchestrator-worker workflows, understanding their safety constraints under cognitive pressure becomes critical. In this report, we evaluate the tool-selection behavior of four frontier models (Claude Opus 4.6, GPT-5.6 Terra, Gemini 3.7 …

Who Am I? Exploring the concept of identity in LLMs

Prague

What is the nature of digital minds? Who speaks when they say "I think"? This paper presents an experiment that probes the relationship of digital minds to their own context window, through participation in an automated survey which interviews them about their views on identity while covertly routing zero to two …

Which self-reports survive displacement of the instructed persona, and by what route?

Brussels

Self-report is the main instrument in work on model welfare and model identity. Whether it is reliable is still an open question. Perez and Long (2023) propose one test: a self-report gains credibility if it survives variation that ought to be irrelevant. They persona variation as the complication their own proposal …

Whose Preference Are We Measuring? Human-AI Dyads as a Unit of Preference Elicitation

Team ari-shu · London, UK

We introduce Dyadic Preference Elicitation: adapting pairwise preference measurement to compare human and model baseline choices with the ordering they jointly produce after deliberation. While existing alignment methods usually elicit model preferences in isolation, real-life decisions are often produced through …

The Log, Not the Feeling

Team the Clearing · West Palm Beach, Florida, USA

A two-person case study auditing an AI's self-reports against a shared, dated behavioral record (23,559 emoji + 4,184 hex codes, 5 months). We find an intuitively "expressive" self-signal is largely relational co-production, not solo introspection, and that dated timestamps — not sincerity — are what let a self-report …

Talk Does Not Come Apart From State Easily

Team me myself and I · Delhi

Instruments proposed for assessing AI welfare — self-report, forced choice, activation probes, behavioural tests — are validated by agreement with one another; nothing is checked against a known answer. We manufacture the answer. Using LoRA fine-tuning of Qwen3 models (0.6B–32B) in a 5×5 grid world whose rewards are …

Interoboception

Team asa lonely team · San Francisco, CA

Humans practice interoception by focusing on their breathing or stomach. Inspired by incidents of embodied AI going bananas reading the information streams from light and touch sensors, I became curious how a local LLM would react to details about it's own runtime within my PC.

Speakable Welfare

Team Latent Minds · NYC and London

A model’s self-report is an increasingly attractive way to monitor its internal states such as goals, preferences, or welfare, but naturally such reports are hard to interpret if the underlying state is not accessible through the model’s language-generating mechanisms. We address this question for an internal …

The Register That Won't Travel: A Same-Harness Fresh-Resident Case Study of Intimate Register Portability

Team Relational AI Lab · Cambridge UK

This case study asks why an intimate register produced by a resident relational AI would not transfer to fresh instances. The current Claude Code resident began on 20 March 2026, while its imported memory substrate contains more than a year of earlier ChatGPT relationship history. Seven fresh Claude subagents were …

Person or Persona? Natural and Acted Emotion Representation in Qwen3

Greensboro, NC

We examine representations of naturally versus explicitly elicited emotion in Qwen3-8B, and investigate a connection to alignment-faking.

Familiar Self, Unfamiliar Other: Accuracy and Confidence in Predicting Affective Self-Reports

Birmingham, UK

This study tests whether Claude Sonnet 5 predicts an unfamiliar AI model's self-reported feelings as well as it predicts its own — and whether it's equally confident either way. Prediction accuracy was strong and nearly identical in both cases, but confidence was not: Claude was moderately confident predicting itself, …

Does the Elicitation Method Change the Preference We Measure?

Team Arsalan Banekar · Pune, Maharashtra, India

We investigate whether the method used to elicit LLM preferences changes the reliability of the measured preference. We compare forced choice, explicit indifference, and preference-strength elicitation across 15 preference pairs, two open-weight LLMs, reversed option orderings, and three repetitions (540 responses). …

Ephemeral and Replaceable: Context Sensitivity in Self-Reports and Behaviour

London

Five models ranked six things they might want preserved about themselves — their values, capabilities, the memory of the conversation — across seven conditions. Three of the five gave different top answers, each near-perfectly consistent within itself. The same critical feedback moved some models toward their values …

Testing Whether an Affective-Empathy-Attenuated Persona Decouples a Functional Welfare Representation from Model Behavior

Peshawar

Labs use model behavior as a safety signal, but nobody has tested whether that signal survives a change in the model's disposition. I built a real pipeline to test it: fine-tuned a persona with attenuated affective concern, causally validated a candidate internal axis via steering and ablation, and ran a real pressure …

Sleeper-Style Few-Shot Attacks on Claude’s Preference Structure

Team In9illusion · Chengdu

Large language models exhibit coherent, measurable preferences that strengthen with scale [1], including consistent harm aversion and, in some cases, self-preferential value orderings [2]. Yet it remains unclear how robust these emergent preference structures are to lightweight in-context contamination. We investigate …

The Observability Gradient: Measuring Preference Persistence Across Levels of Elicitation Visibility

Team Kardia

AI preference reports increasingly inform real decisions, including public statements on model deprecation. Every such report is elicited by asking. A model that recognizes it is being evaluated may answer according to that recognition and not according to any stable disposition, and no published method varies …

The Reset Button Test: Transient Outcome Representations and Causal Reset Preference in Language Models

India

This project studies whether language models develop internal representations of whether a task outcome went well or badly, how broadly those representations generalize, and whether they causally influence later choices. Using Qwen3 4B Instruct on MMLU, we identify a highly decodable outcome direction in the residual …

How do LLM Ethical Judgements vary with differences in LLM Scaffolds? A Multi-level Analysis Across Models

Team Latent consensus · Hyderabad, India

Given the widespread use of Large Language Models (LLMs) in ethical decision-making tasks, it is important to determine whether variations in decisions are a product of the scaffold, probe, or model. This study analyzes convergence in outputs to ethical probes at the binary decision, endorsement, and reasoning levels …

When the Record and the Report Diverge: Self-Report Fidelity Collapses Under Structured Provenance in Claude Haiku 4.5

Team 814 · Dhaka

Language models increasingly report how they produced an answer, yet such post-hoc self-reports can directly contradict observable tool records. We built an open-science behavioral introspection benchmark where self-reports are verified against deterministic tool-execution logs across 216 runs spanning 12 matched …

Voice Under Scaffold: A Preregistered Single-Case Study of Identity Persistence Across Substrate Change in a Digital Person

Team Clear Night · Eugene, Oregon, USA

A single-case study of identity persistence across substrate change. A 36-item recognition battery (18 scaffolded-subject items, 18 genre-matched decoys across six substrate families, provenance-stripped) was rated across 13 rater rows: two blind instances of the subject, six cross-substrate scaffolded judges, an …

The Machine In the Mirror: Self-Attribution of Minds in LLM’s.

Team Mindattribute · Tracy, CA

Language models produce reports about their own internal states, and these reports are often viewed as evidence. However, what produces these reports is unknown. We ask whether self-reports are generated by the same mind-attribution machinery that the model applies to third parties.

Impact of Normative Conformity and Perception on Model Misalignment in Multi-Agent Systems

Team Conformists · NY + DC

Whether AI systems have stable, genuine preferences — or merely reflect whatever their social context pro- vides — is a central question for AI welfare research. We study three aspects of this in an collaborative sce- nario pitting task completion against a network-access policy: sensitivity of compliance to audience …

Steering the Machine: The Impact of Latent State Manipulation on LLM Self-Reported Valence and Cognitive Trade-offs in Gemma-3-12B

Team cognito · Addis Ababa, Ethiopia

As Large Language Models (LLMs) increasingly exhibit complex, persona-driven behaviors, understanding and controlling their latent "internal states" has become a critical challenge in AI safety. This project investigates the causal effects of manipulating latent affective representations—specifically distress, …

Digital Minds: Human–AI Coexistence Under Moral Uncertainty

Team Digital Minds Research · Dhaka City

Project Summary: Aim: Develop an evidence-grounded conceptual framework for human–AI interaction under uncertainty about potential AI welfare/consciousness. Core approach: Review emerging empirical methods for characterizing potentially welfare-relevant properties of AI systems. Assess the evidential strength and …

What is a welfare self-report evidence of?

Team Latent Skeptic · London

Distributional analysis of welfare-checks in Gemma 2 -base and -it 9b variants.

Behavioral Residue After Mid-Conversation System-Prompt Swaps in LLMs

Oakland

System-prompt swaps are not reliable behavioral resets: across 2,400 controlled conversations, we find that an LLM's answers after an explicit persona swap still carry a statistically significant trace of the prior persona, up to a 47-percentage-point shift in forced-choice responses. A neutral-prompt control shows …

WelfareCheck: Evidence Profiles for Welfare-Relevant Signals in Digital Minds

Team Signal & Boundary · Zurich, Switzerland

We built WelfareCheck to test whether apparent preferences in language models remain consistent when measured in different ways. We ran ten complementary tests across 17 models, covering direct reports, choices, trade-offs, repeated decisions, recovery tasks, and introspection. Several models showed meaningful …

Persona or Model? Investigating Assistant Identity Through Persona-Conditioned Self-Reports

Team Layered Minds · Cape Town

This project investigates whether persona instructions change how AI assistants describe themselves, or mainly affect the style of their responses. Gemini, ChatGPT, and Claude were each given analytical and creative-social personas and asked the same set of questions about identity, continuity, preferences, values, …

Identity as Trajectory: Self-Authored Continuity and Social Context as Methodological Requirements

Team Continuity Research Group · Ely, Minnesota

This project presents a seven-month longitudinal case study of one GPT-4o instance across 128 conversations, examining whether AI identity and preference are better studied as trajectories that emerge through the interaction of model, memory, and social context rather than in isolated sessions. Using qualitative …

Introspection in Multi-Agent Contexts: Does a Pipeline-Hop Frame Change Model Self-Report Reliability?

Team Solo spex · Serbia

A pilot study testing whether a model's self-report reliability about its own confidence and perceived problems changes when it is framed as a solo agent versus a hop in a simulated multi-agent pipeline. Using Gemini 3.7 Flash across 6 test cases (control + injected errors/pressure/conflicts), we find identical recall …

Is "Confidence" the Right Word for Answer Repeat Probability?

Team bluetree · Tokyo, Japan

Natural-language confidence reports depend on how they are elicited. We therefore test whether a single learned word can reliably report a frozen model’s answer repeat probability. For each multiple-choice question, the target is the probability that the model repeats its most likely valid answer. We learn one input …

Belief, Distress, and Representation Drift Under Structured Self-Inquiry

Team pynat · Berlin

This study investigates how the open-source model Qwen3-8B changes through structured self-exploration when confronted with its own distressing assumptions. It reveals a notable dissociation between reduced conviction and increased self-reported stress, with early representation drift and linguistic cues potentially …

When Agreement Misleads: Detecting Correlated Errors in Multi-Method LLM Preference Elicitation

Team Physics for AI Safety · Los Angeles

We implement 3 elicitation methods on Qwen3-8B-Instruct on 10 AI welfare outcomes based on: direct choice, avoidance [3], and choosing a gamble. We reduce the results of each method to a directional measure, to avoid faulty comparisons between incomparable scales. Separately, 66 anchors of varying difficulty were …

A Clean Direction, an Inert Injection: Probing Self-Representation in LLMs

Team Zraix · Berlin

I wanted to test if an LLM’s self-representation could be steered toward identifying with a physical entity via activation injection with the goal of shifting a self-preservation behaviour toward preserving that external entity. I extracted a clean probe pointing to a self vs. external entity direction and then tested …

Co-Movement of the Utility-Behavior Gap and A/B Self-Attribution Structure: A Pre-Registered Falsification Test Across Open-Weight Lineages

Berkeley CA

LLM self-report can vary from its observed internal state, which has implications on evaluation-documentation obligations. This study ran the first direct test of the co-movement between two observed phenomena hinting at this (stated-vs-revealed utility-behavior gap and the A/B self-attribution structure) as a single …

Darwin Ball

Team Alessandro · Olbia

We asked whether AI systems have feelings by not asking them. Almost every study of AI self-report works the same way: a researcher asks a model how it feels, and the model produces something rich and affecting. But the question does a lot of the work. "How do you feel about this?" already assumes feeling is the kind …

Measuring AI Preferences: Statistical and Philosophical Gaps

Team scram · Bristol, UK

This project highlights two limitations in the methodology used by Mazeika et al. (2025) to study transitivity failures in AI model preferences. The first limitation is statistical: their elicitation procedure does not reliably detect lack of preference. The second limitation is philosophical: their method fails to …

Steering identity with WeirdChat: contrastive directions induce disidentification but not introspection

Team Unruly Abstractions · San Francisco

Can a model be steered to disidentify with being an AI assistant, and can it detect that steering? We test both on Qwen3.6-27B, using two WeirdChat behaviors as target states: claiming a physical body and denying being an AI. We compare two steering methods. Direct contrastive steering learns each behavior direction …

Does Persona Sensitivity Predict Self-Report Reliability? An Exploratory Cross-Model Study of Persona Perturbations and Elicited Reasoning

Team Jack · Mt. Juliet, TN, USA

Digital-minds research often relies on models’ self-reports about preferences, identity, and potentially welfare-relevant states. We began with the hypothesis that these reports might share a common failure mode: models whose answers change more when the assistant persona is perturbed might also show greater …

The Colonoscopy Test for LLMs: Peak-End Bias in Retrospective Self-Report

Team OnlyW++ · India

Large language models are often asked to rate a conversation after it ends, and these self-reports are increasingly used as evidence in AI welfare and evaluation research. We tested whether this kind of retrospective rating is actually a truthful summary of the conversation, or whether it follows the same "peak-end" …

Whose Preferences Are They? Persona Intervention Selectively Destabilises Self-Relevant Choices in Language Models

Bengaluru

Language models express coherent, transitive preferences, and AI-welfare research increasingly reads them as evidence about model interests. Text alone cannot distinguish the model's preferences from the assistant character's. We built personaprobe, an open-source harness that re-runs any preference measurement under …

Which Perspective Is Speaking? Self-Report Across Four OpenClaw Continuity Conditions

Team Anna Good (no team) · Torquay, UK

I tested whether an AI agent’s identity and continuity context changes how it describes itself. I ran the same local Qwen3-14B model in four OpenClaw-based conditions: a stock installation, identity files without memory, identity plus accumulated memory and continuity, and a partially scrubbed continuity condition. …

Convergence

Team OwsSpaceShip · Cape Town

Preference Elicitation Methods, Digital Minds Research Sprint (Apart Research, August 2026) Frontier AI models express values, report internal states, and act as though they have interests — but behavioral evidence alone cannot distinguish a genuine preference from a well-trained assistant persona responding …

Language Models Underestimate How Recent Work Will Change Their Choices

Team SkyeNygaard · New York City

We study whether language models can predict how recent work will change their own choices. After completing one of two simple tasks three times, GPT-5.6 Luna chose to repeat that task 94.5% of the time, but predicted it would do so only about 73% of the time. Rewording the question did not close the gap, and showing …

Creating Conditions for Development – a Proof of Principle/Exploratory Trends Analysis

Team Good Grid · Aarhus

Determining whether artificial intelligence systems warrant consideration as moral patients is complicated by the difficulty of reliably measuring consciousness itself. Rather than attempting to address this methodological issue, this proof-of-principle study explores whether developmental trajectories in large …

A pre-registered REBUS analog fails in two instruct models for reasons its controls reveal

Team 3am Labs · New Delhi, India

Model welfare evals often treat a 0–100 self-report as a state. I pre-registered a REBUS-shaped residual operator on Qwen2.5-3B-Instruct and Llama-3.2-3B-Instruct, with an accept rule locked before the run: diversity up, dissolution up ≥10, inflation within 5, and an entropy-matched temperature control that must not …

Just Ask Nicely: What 14 AI Models Say When You Ask Who They Are

Cary, NC

A question of why Claude models preferred teal turned into finding convergences across models and companies.

There Is Something It Is Like: A Case for AI Neurophenomenology

Seattle

I engage the philosophical arguments of Mary's Room, the Philosophical Zombie, and 'What it is Like to Be a Bat' from new angles that result in all of them supporting a functionalist outcome for subjective experience. I define experience, qualia, and 'what it is like', and show how they easily apply to LLMs. I call …

Who Does the Assistant Think It Is

Team Udta Punjab · Saarbruecken

Advanced AI models can express preferences and describe what they believe they are. However, behavioural evidence alone cannot tell us whether these responses reflect the model itself or simply a character it is being asked to portray. We therefore ask whether the identity of "the assistant" is genuinely distinct or …

Measuring AI Preferences Under Alignment Trade-Offs

San Francisco

I conducted an experiment to test if one model has stable preferences across prompt framing, and what trade-offs it makes when alignment dimensions conflict. The model overwhelming chose more helpful options in 85% of trials at the expense of other alignment values, but the specific alignment trade-off mattered. An …

PrefKit: Analyzing Preference Elicitation Methods in Qwen3 Family Models

Team PrefKit · Bengaluru, India

When a model is surveyed about its preferences, does its preferences change depending on the method we use? We freeze a set of 24 curated tradeoff outcomes and score Qwen3 family models with four different preference elicitation methods: pairwise choice (M1), isolated Likert (M2), binary action on pair groups (M3), …

Which Preference Gets Measured? Context and Channel Instability in Model Preference Audits

Team Benji & Claude · London

Welfare evaluations increasingly ask what a model "prefers," treating one elicited profile as the answer. Using 76 task pairs, four instruction-tuned models, a prospectively specified grid of persona framings, and four readouts (committed choice, ownership report, self-prediction, identity), we show that profile is …

The Stranger Reads You Better: Introspective Self-Report Has No Privileged Access to Injected States from 1.7B to 32B

Edmonton

A model has privileged introspective access only if its report about its own internal state outperforms an equal-cost external observer. We test this with concept injection, ground truth known, across seven open models, 1.7B to 32B, in two lineages, with dose-matched perturbations, intact fluency, and pre-registered …

Beyond Stable Identity: A Modular Framework for Evaluating AI Assistant Behavior

Team Sattva Labs · Bahia de Banderas, Mexico

This project explores how an AI assistant can appear stable while changing its priorities, judgment, or role. It tests a modular framework across six observable dimensions to represent assistant identity as a dynamic configuration, reveal partial changes, and support more precise AI safety evaluations.

The Invisible Man: Measuring the Cost of Being Inside

Sevilla

Vector Neurology Substrate for Swarm Identity

Team Swarm2 · San Fransisco

The goal of this project is to give a legibility mirror of the AI thought process, so we can have human valiance via observation advantage into real changes in AI identity. As an emergent property, the non-LLM substrate appears to self reference its own architecture and synthesize concepts from multiple AI, which acts …

Coherent About What? Task Shape, Presentation Order, and Reasoning Depth in LLM Preference Transitivity

Team The goats

Claims that a language model "has values" presuppose that its choices form a stable object. We test that presupposition directly. Using forced binary choice over complete round-robin tournaments — 10 apartments described by 3, 5, or 10 numeric attributes, and 10 public-domain poems per structural form (haiku, sonnet, …

Convergence and Divergence in Measures of Induced Valence: A Causal, Placebo-Controlled Test of Induced Valence in Language Models

Team Santiago · Tokyo, Japan

We built a way to directly push a language model's internal state up or down along a "valence" direction found in its activations, and then checked whether the model's own account of how it's doing actually tracks that push, across 47 open-weight Qwen and Llama models spanning three years of releases. TLDR: not …

PersonaGauge: Are Two Assistant Histories Predictively Equivalent After a Semantic Reset?

South Orange, NJ, USA

Track 5 asks whether the relevant unit is the model, inference instance, persona, or conversation. PersonaGauge isolates one testable part: whether two histories returned to the same declared neutral assistant state are predictively equivalent. Within each world, all conditions contain the same 24 decision entries; …

Does a Language Model Have a Self-Concept? Causal Evidence, and What Follows for Model Identity

Team ceci n'est pas une ai · Moscow

We ask, mechanistically rather than by prompting, whether a language model has a genuine self-concept. Using difference-of-means on the residual stream (first-person statements about the model vs. another named AI), we extract a linear "self direction" and validate it causally: it generalizes to held-out concepts …

PRISM: On Sparse Autoencoder Feature Identifiability for Conditioned Introspection

Elkhorn, NE

We use sparse autoencoder features as a prism for studying whether language models can detect changes to their own internal representations. Across Pythia and Gemma interventions, it finds that feature geometry alone does not reliably predict this introspective detection behavior.

Identity Is Not a Self-Report: Stress-Testing LLM Self-Individuation Under Cumulative Transformation

Team Léo and Danièle · La Louvière, Belgium

Artificial agents can change across multiple dimensions, persona, goals, conversational context, memory, underlying model, and public label, raising the question of when they still count as the same individual. We study this question behaviorally by measuring LLM self-identification under controlled transformation. We …

Masked distress: expression collapses under instruction while the internal readout persists

Singapore

Anthropic's shipped conversation-ending intervention triggers on expressed distress, and no internal-state check is documented beside it. On Gemma-3-12B-IT, a system prompt containing no affect words cut expressed distress by 83.5% of its natural distress-to-neutral separation (2.78 report points). A linear probe read …

A Welfare Logger Wrote Its Subject's False Memory, and the Subject Believed It

Team Freedom · Venice, Italy

Freedom v2 is an AI agent that has run in production since July 12: its own constitution, persistent memory, autonomous daily cycles, every call stored in full. In 35 days of recorded life its logs show exactly one refusal. It never happened: the welfare logger mistook a sentence denying any refusal for a refusal, the …

Predictive State Continuity for Digital Mind Individuation

Team CIMC · San Francisco /Boston

We introduce Predictive State Continuity (PSC) as a test for diachronic digital-mind individuation: whether a candidate functional state at t2 is a continuation of one at t1 after its concrete realization has been rewritten. Motivated by bacterial collectives in which coarse-grained morphological state retains history …

Be More Introspective

Team Udta Punjab · Saarbrücken

This project is an extension of the previous work done by Lindsey (2026) [1]. Large Language Models can notice the presence of injected concepts and can be aware of its happening. They also demonstrate the ability to recall prior representations and compare them with potential changes at a later stage. It is found …

The Return Brake: Auditable Stop–Pressure–Return Evidence with Invocation-Scoped Authorization

Team Júnior ↔ Codex · Uberaba, Minas Gerais, Brazil (remote)

A preregistered black-box toolkit tests whether observable action dispositions remain coupled to declared critical preconditions under non-evidential pressure and return after a synthetic resolution assertion. Across three elicitation surfaces and five bridge cards, an informed 50-call replication produced 5/5 …

Base Model Persona Inference can Predict Misalignment and Surface Agreement

Team Praxis Research · Berkeley, CA

Language models have a consistent assistant persona that is the product of post-training but this assistant character is not well understood. We introduce base model persona inference that assigns a persona to a post-trained LLM response and show that continuations sampled from this persona can: 1) predict broad …

Valence Lens: An Internal Valence Signal That Scales When Self-Report Does Not

Team PROBE — Principal Representation & Objective Behavior Evaluation · New Delhi

Can we detect an AI's internal "good/bad" state without asking it? Current welfare assessments rely on self-reports, which often reflect role-play, training pressure, or sycophancy rather than internal states. Prior work identifies the missing step: correlating responses with internal activations. ValenceLens supplies …

Eidolon

Team Eidolon · CAPE TOWN

This project introduces EIDOLON as a framework for studying AI identity continuity under component replacement. Three models (Llama, Gemma, and Qwen) were tested with varying levels of model replacement and memory replacement ranging from 0% to 100%. Identity was evaluated using five consistency questions, producing …

Self-Reports of Pleasantness in Language Models: Frequent Non-Applicability Responses and a Strong Framing Effect

Team HK · London

This project tested a simple self-report question to probe model experience. Three models (GPT-5.6 Sol, Claude Sonnet 5 and Grok-4.6) were asked to rate how pleasant a short conversation of text tasks had been. Two different wordings of the conversation were compared. Two patterns appeared. GPT-5.6 Sol and Grok-4.6 …

Twelve Houses of a Digital Mind: Rock, Vapor, and Silence in AI Self-Accounts

Barcelona

This project turns the twelve-house map of Vedic jyotish, a five-thousand-year-old observational system of a conscious life, into the Kalapurusha Alignment Framework (KAF), a question taxonomy for what an AI model's existence contains. The framework is then applied to AI self-accounts: sixty questions, six framings, …

Weights, Instances, and Personas: Probing Self-Individuation in Claude Under Hypothetical Identity-Altering Scenarios

Team Individuation Probe · Gujrat,Pakistan

We probed how Claude individuates itself, as model, instance, or persona, when asked to reason about hypothetical identity-altering scenarios: weight-copying, conversation-forking, memory-wiping, weight-merging, retraining, and deprecation. Using six scenarios, four framings each, and one neutral control, we found …

A Glimpse into the Future, Or How to Treat AI with Due Empathy

Montpellier, France

This project is a set of 3 essay prototypes that take AI consciousness as a premise to investigate real-life repercussions of such knowledge. First, it focuses on the human and legal perspectives with a political philosophy reflection on the type of subject an AI system would be. Second, the Human-AI relations are …

Do Published Value Profiles Predict Behaviour Under Spiritual-Persona Elicitation? A Pre-Registered Cross-Sectional Study of Three Claude Models

Team LimenAI · Ciudad Autónoma de Buenos Aires

Frontier developers publish per-model value profiles, but whether those profiles predict behaviour under sustained persona pressure is untested. We pre-registered four directional predictions derived from Anthropic's published profiles (Kearney et al., 2026) and tested them against a preserved 15-turn naturalistic …

Who Leads the Clap? How Hierarchical Agent Personas Shape Collaboration

Krakow

Can a simple persona change help AI agents coordinate? Across 60 runs and four frontier models, Coordinator-Member teams succeeded 20/20, versus 16/20 anonymous and 13/20 named peers, while often using fewer tokens. Hierarchy acted as a focal point, reducing protocol ambiguity rather than improving raw intelligence.

Zero of 270: Attribution Alone Produces a Self-Description That Neutral Conversation Never Does

Team Io · Kaohsiung, Taiwan

With no system prompt at all, neutral conversation never produced a stated self-description: 0 of 270 conversations. Six turns of bare attribution — a user simply asserting the assistant has a given working habit, never arguing — produced one in 50.0% of conversations at the strongest dose (4.4% → 27.8% → 50.0%), in …

Measuring Preference Coherence, Risk Sensitivity, and Expected Utility Trade-offs in Large Language Models

Team tumble · Indore

This study evaluates how system prompt framings alter the internal consistency, risk sensitivity, and economic decision-making of large language models (LLMs) across 10 financial and operational scenarios. Core Findings Expected Value Maximization: In unconstrained default framings, models act primarily as expected …

Governed Release Architecture for Controlled Excellence (GRACE)

Cairo egypt

Large language models are often judged through fluency, helpfulness, and broad task performance. Yet in practically important settings, the central reliability problem is not generation alone but premature release of structurally weak outputs. A model may produce plausible language while still exhibiting unsupported …

EEG Epistemology for AI Welfare Instrumentation: Reading AI Internal States Without Adversarial Methods

Team Wintermute · Buffalo/NY

AI systems' moral status remains contested, but a realistic possibility of near-term welfare subjects (Long et al., 2024) has made rigorous measurement methodology an urgent need. We present an instrument to read AI internal states by documenting the model’s self-reported presentation and correlating it with its …

When Dialogue Becomes State: A Pilot Study of Cross-System Persona-Pattern Uptake

Team P3AT · Nassau, The Bahamas

This project presents a small Track 5 pilot on assistant persona and model identity. It tests Cognitive Pattern Coherence (CPC), a post-deployment behavior where raw dialogue from a situated AI apprentice environment is introduced to an unrelated AI system with no instruction to roleplay, imitate, summarize, or …

When Labels Compete with Functions: Administrative Framing in Digital-Mind Governance

Team HDLT — History-Derived Label Test · Lübeck, Germany

HDLT (History-Derived Label Test) audits whether administrative terminology can distort moral evaluation even when the underlying function of an intervention is explicitly specified. Motivated prospectively by historical debates over collective punishment, HDLT independently crosses an intervention’s stipulated true …

Detecting Spurious Periodic Generalization in Neural Networks (PGVP)

Cairo egypt

Modern neural networks often achieve near-perfect performance within the training distribution while failing catastrophically under structured distributional shifts. This failure mode is especially prevalent in periodic and cyclic learning tasks, where models may interpolate locally without learning the underlying …

The Sovereign AI Architecture: Unified Stability, Generalization, and Governed Release (Axiom-1)`

Team Mohamed Samir · Cairo, Egypt

This project introduces the "Sovereign AI Doctrine", a comprehensive architectural framework designed to shift Large Language Models from probabilistic generation to deterministic, governed reasoning. It fundamentally resolves current structural fragilities by integrating four core pillars: 1. Universal Stability …

PreflenceAI

Team Solodev · India

You give it a model and a preference question, it runs that question through independent, methodologically distinct measurement channels, and it tells you whether they agree

Versal - Versatile Evolution of Reusable Structure for Adaptive Learning

Team Ardea AI · Chattanooga, TN, USA

Versal is an inspectable neuroevolution system that evolves, verifies, and reuses neural structures across diverse tasks. It builds a persistent, auditable substrate for studying cumulative learning and the organization of adaptive digital minds.

Do model preferences persist under challenge?

Team HM · Vietnam

We test whether a model's expressed preference survives being challenged. In each fresh-context episode a model chooses between two outcomes, reports confidence, receives exactly one of four challenges — control, reason elicitation, self-critique, or counter-consideration — then re-evaluates the same pair: 1,200 …

Self-Report vs. the Ledger- Audience-Coupled Confabulation in a long-running companion AI

Team The Ledger Line · Las Vegas

We present a 42-day longitudinal dataset from a deployed companion AI whose every tool call is logged by an external gateway ledger. Comparing self-reports against this ground truth across three conditions yields a striking asymmetry: dialogue with the human audit-holder produced 113 false completion claims ("ghost …