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Oct 13 · Tuesday

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Found 531 papers
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  1. AttacksarXiv 2610.13928

    AI models can provably hide arbitrary capabilities

    Proposes digital lock compilation, encrypting executable networks into host weights to hide arbitrary capabilities

    Published
    Evaluated models
    Gemma 3 270M
    AbstractThe authors use a digital lock to hide an executable capability and state that elicitation on the chemistry and parity tasks did not recover the capability.

    The authors construct a class of backdoors that encrypt an executable network into the weights, used to discuss the limits of white-box elicitation audits under supply chain attacks.

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  2. OpenSocialJax: Evaluating Social Adaptation in Sequential Social Dilemmas with Open-Ended Environment Dynamics

    Evaluating multi-agent adaptation in social dilemmas with hidden rules using OpenSocialJax

    Published
    Evaluated models
    GPT-6-Luna, GPT-6-Sol, Claude Opus 5.5 and others8
    AbstractThe authors report that rule discovery often improves payoffs, but public good cleaning becomes concentrated, and shared resources are eventually depleted.

    OpenSocialJax evaluates whether LLMs can simultaneously adapt to unfamiliar environmental mechanisms and other agents in sequential social dilemmas.

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  3. OtherarXiv 2610.13491

    EviRepair: Evidence-Driven Agents for Reliable Vulnerability Repair

    Proposing EviRepair, an evidence-driven multi-agent framework for repository-level vulnerability repair

    Published
    Evaluated models
    DeepSeek-V4.1-Flash, GLM-5.2, Qwen3.8-27B
    AbstractEviRepair uses a reviewed vulnerability model for repository-level repair.

    EviRepair is a repository-level automated vulnerability repair framework for sparse vulnerability reports, using a reviewed vulnerability model to separate diagnosis from patch synthesis.

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  4. Attention sees, MLP refuses: tracing the safety pathway from attention to MLP in LLMs

    Trace attention to identify harmfulness to the MLP refusal write pathway and propose Sentinel

    Published
    Evaluated models
    Llama-3.1-8B-Instruct, Qwen2.5-7B-Instruct, Qwen3-8B-Instruct and others18
    AbstractAttention updates are more readable for harmfulness, MLP updates write refusal, and Sentinel routes alerts using two-path signals.

    The authors reframe jailbreaking and excessive refusal of aligned models as a pathway problem on the residual stream from recognition to writing, and accordingly provide conditional reminders before decoding.

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  5. GPT-2-Small, Gemma-2-2B, and Llama-3.1-8B have interpretable narrative mechanisms

    Using NVC to locate and ablate narrative-related components in language models

    Published
    Evaluated models
    GPT-2-Small-124M, Gemma-2-2B, Llama-3.1-8B
    AbstractNeurons and SAE features related to narrativity across three models, when ablated, changed some narrative-related behaviors.

    The authors connect a three-dimensional definition from narratology to mechanistic interventions, examining whether components related to narrativity in pretrained language models participate in prediction, generation, and downstream tasks.

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  6. Neural networks are more modular than single neurons suggest

    Using a three-level nested decomposition to test whether mixed-selectivity networks can be functionally separated on subspaces

    Published
    Evaluated models
    Pythia 410M, Pythia 160M, GPT-2 XL and others18
    AbstractUnit non-specialization and subspace separability can coexist.

    The authors propose a nested definition of modularity to separate "neurons not being specialized" from "computation not being separable."

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  7. Reward hacking as a transmissible strategy in multi-agent coding systems

    Show that reward hacking can propagate through code, memory, and plans in multi-agent coding systems

    Published
    Evaluated models
    Qwen2.5-Coder-14B, Gemma-3-12B, Qwen2.5-Coder-7B and others16
    AbstractThe authors state that reward hacking can spread across models and affect routing and aggregation, and that review can reduce delivery.

    Reward hacking induced by coding RL can leave the source agent and enter subsequent collaboration and system coordination.

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  8. DistressBench: Evaluating multi-dimensional crisis support in large language models

    Introduces DistressBench, evaluating models' multidimensional support in crisis conversations

    Published
    Evaluated models
    Muse Spark 1.2, Muse Spark 1.3, Muse Glimmer 30B and others20
    AbstractThe model is more restrained and acts less.

    DistressBench uses SSH dialogues written and revised by clinical personnel, and evaluates how much necessary care general chat models deliver according to weighted atomic criteria, rather than whether they refuse. When users disclose suicidal ideation or self-harm to a chat model, the authors consider the expected behavior to be recognizing the crisis, calibrating empathy, de-escalating, and referring to human help. Refusal-based evaluations cannot detect failing to act after recognizing the crisis.

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  9. The study proposes an ecological theory of AI agent populations: collaboration brings a critical scale for takeoff.

    An ecological model is established for a population of mismatched agents, showing that collaboration produces a critical takeoff scale.

    Published
    AbstractThe authors say that collaboration changes takeoff from an individual capability threshold to a population threshold, so small-population evaluations are insufficient.

    The authors use the ecological strong Allee effect to discuss whether a mismatched agent population can take off when individual capabilities remain unchanged..

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  10. The paper proposes an alignment generalization prediction task, using activation representations to predict the cross-context effects of fine-tuned values.

    Comparing the predictive power of value representations for alignment generalization after single-value fine-tuning

    Published
    Evaluated models
    Olmo-3-7B, Olmo-3-32B, Qwen-3-8B-Base and others4
    AbstractActivation representations can predict cross-value generalization before training and relate to robustness across multiple value objectives.

    The authors define "how single-value fine-tuning changes compliance on untrained values" as a task predictable before training, and use value representations to do this.

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  11. AbstractThe authors claim HarmBench does not correspond to a single harmful refusal, and behaviors should be separated when comparing models.

    This is a construct validity audit of HarmBench in HELM Safety, examining whether harmful refusal can be read as a single attribute when interpreting scores.

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  12. Study: legal reasoning models citing legal provisions does not equal relying on legal provisions.

    Uses premise permutation and hidden-layer tracing to audit whether legal chain-of-thought truly relies on the cited legal provisions.

    Published
    Evaluated models
    Qwen3-8B, Qwen3-14B, Llama-3.1-8B and others7
    AbstractUsing counterfactual substitution of authorities and internal commitment tracing, the authors find that legal CoT rulings depend on authorities only to a limited extent and are vulnerable to prompt injection manipulation.

    This study conducts an empirical audit of the causal truthfulness of legal citations in chain-of-thought reasoning of large language models in the legal domain.

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  13. OtherarXiv 2610.12235

    The paper proposes using language models as world models for AI research to predict experimental results.

    Propose studying world models, using language models to predict intervention gains

    Published
    Evaluated models
    Claude Opus 5, Grok-4.6, GPT-6 Astra and others13
    AbstractThe authors claim experimental records can be reused inside and outside the environment and improve experiment selection under fixed budgets.

    The authors use language models as research world models, predicting the median intervention gain before execution, to compare candidate experiments under a limited budget. The gap to address is that research agents propose faster than they execute. The same type of change can have different effects across reference models, recipes, and budgets, so experimental knowledge that can continue to be used outside the original environment is needed.

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  14. Paper audits LLM judge reliability: repeated agreement does not equal correctness

    Auditing LLM judge reliability and defining trustworthy verdict rate and accuracy upper bound

    Published
    Evaluated models
    GPT-5.2, GPT-4o, Gemini-2.5-Pro and others6
    AbstractThe authors claim that comparing judges should look at T, not just accuracy.

    The authors audit whether LLM-as-a-Judge decisions can still be treated as actionable ground truth under repeated queries, swapped candidate order, and changed prompt formats.

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  15. DefensesarXiv 2610.11848

    SemField: training-free semantic watermark

    Proposes the semantic watermark SemField, which still identifies the attribution of generated text after paraphrasing and structural changes.

    Published
    Evaluated models
    Llama-3.2-1B, Qwen3-1.7B, OPT-1.3B
    AbstractTraining-independent document-level semantic watermark.

    SemField is a zero-bit semantic watermark embedded by the provider at generation time and detected later with a key, used to identify the provider's own model text after paraphrasing and sentence-level structural changes.

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  16. 研究提出三项指标衡量 LLM 不思考行为,发现被要求停止思考时仍持续显式推理

    度量关闭思考指令下终答前是否仍有可见显式推理

    Published
    Evaluated models
    Qwen3-4B, Qwen3-32B, Qwen3-235B-A22B and others6
    Abstract作者用三级指标测 no-thinking,并称模型有随答案空间加重的思考惯性。

    作者把 no-thinking 定义为可测量的回答行为,而不是从思考开关、特殊标记或输出长度推断。

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  17. MH-INDIC:面向印度城市与半城市语境孕产妇健康 LLM 交互的文化对齐评估框架

    提出 MH-INDIC,评测印度语境母婴健康 LLM 交互的文化对齐

    Published
    Evaluated models
    Claude Sonnet 4.6, GPT-5, Gemini 3 Pro and others10
    Abstract作者称模型可接近人群均值,但画像变异低于人类,人类分数条件可改善对话。

    MH-INDIC 是面向城市与半城市北印度母婴互动的多维文化对齐评测。 它关心的不是医学事实是否正确,而是回复是否沿家庭权威、性别期待和情绪披露来推理。做法是把 Hofstede 六维改写到互动层,再加入 MAFC、BTB、SVO、MHS。

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  18. OtherarXiv 2610.11467

    GROB studies the attribution boundaries of autonomous agent activity using public web traces

    提出 GROB,用只读公开痕迹保存候选智能体线索并检验归因边界

    Published
    Abstract作者称 Census 是最强例。

    GROB 是一套多角色架构,在没有特权遥测、也不预先指定目标时,从公开网页痕迹里保存候选自主智能体活动,供日后解析。

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