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181 records found · page 7 of 10
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Journal article
A Safety Memory Is a Declassification Channel: Why Origin Binding, Taint Tracking and Memory Isolation Do Not Secure the Records an Agent Writes About Attacks
Two lines of work on language-model agents have converged on the same component from opposite directions. One proposes that an agent keep a persistent safety memory, so that an attack seen once, a request refused once or a source found untrustworthy once continues to inform decisions in…
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Journal article
Predicting Your Own Failure Is Not Predicting Another Model's Advantage: What Router Results Establish About Agent Self-Knowledge, and the Incremental-Validity Test Nobody Has Run
A common reading of the LLM routing literature holds that delegation works without self-knowledge: a router allocates queries using external features of the task and observed outcome statistics over a model pool, and never consults the candidate model's opinion of its own competence…
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Journal article
An Unknown Unknown Is Defined Relative to an Oracle: Four Places External Judgement Enters Blind-Spot Discovery, and Why the Self-Detection Results Do Not Remove It
An unknown unknown is standardly defined as an input on which a model makes a high-confidence mistake. The definition is not incidental to the research problem; it fixes its shape. A quantity defined by the conjunction of high confidence and error cannot be recovered from confidence…
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Journal article
Four Things Called Forgetting: Interference, Transience, Reset and Unlearning Remove Different Things, Why Forgetting Looks Easy by Accident and Hard on Purpose Only Under Different Instruments, and the Savings Measurement That Would Tell Them Apart
Machine learning uses one word for four operations. Catastrophic forgetting is damage that fine-tuning does to earlier capabilities. Transience is the fading of individual training examples during ordinary training. Resets deliberately reinitialise part of a network to restore its…
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Journal article
Consensus Too Soon, or Agreement From the Start? Shared Prior, Social Coupling and Pool Coverage in Decentralised LLM Collectives, and Why Prompted Diversity and Model Heterogeneity Act on Different Terms
Groups of language-model agents that exchange answers and settle on a common one are now a standard way to build decentralised decision systems. A common worry is that they agree too soon: agents copy each other, diversity collapses, and the group loses the independent errors that make…
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Journal article
How Many Classes Are Out There? In Category Discovery the Class Count Is Granularity Carried Over From the Labelled Set, Supplying It Can Supply the Taxonomy, and Without It the Count Is Set by a Hyperparameter
Category discovery asks a model to sort an unlabelled image collection into classes, some of which it has never been shown, using a labelled subset of other classes as its guide. Almost every method needs one number before it can produce an answer: how many classes the unlabelled data…
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Journal article
Refusal Is Not a Rate: What a Context-Conditioned Refusal Policy Would Have to Specify, Why the Benchmarks That Score Context Assume It Is Verified, and the Provenance Term No Instrument Prices
A language model that refuses a harmful request and a model that refuses a harmless one produce the same event, and most of the literature on the jailbreak/over-refusal trade-off counts both as one refusal rate. The standard proposal for escaping the trade-off is to make refusal depend…
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Journal article
Checked at Every Step Is Not Checked as a Whole: Two Senses of Plan-Level Safety for LLM Agents, and Why Decomposition Attacks Exploit the Gap Between Them
Most deployed safety mechanisms for LLM agents judge one unit at a time: a single tool call, or a single (observation, action) pair (Choi et al., 2026). A separate literature asks whether that is the right unit to judge at all. Jones, Dragan and Steinhardt (2024) show that a task no…
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Journal article
Compressed Once, Read Many Times: Why Prompt-Compression Results Do Not Transfer to Agent Memory, What the Query-Agnostic Line Already Showed, and the Two Properties of a Memory Write No Protocol Yet Scores
Language-model agents that remember across sessions compress what they store: they summarise dialogue, extract facts, or evict cache entries, and then answer later questions from what is left. The compression ratios used to justify these designs come mostly from prompt-compression work…
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Journal article
Does a Model Forget Differently When the Data Is Its Own? RL's Retention Advantage and Model Collapse Are Claims About the Same Loop, and No Study Has Measured Both
Two literatures make claims about what happens when a language model trains on data that resembles its own distribution. One asks whether reinforcement learning forgets a model's prior capabilities less than supervised fine-tuning does, and answers yes: on-policy RL is implicitly biased…
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Journal article
Curriculum Is Three Claims, Not One: Ordering, Selection and Decomposition in RLVR, and the Random-Order Control Almost Nobody Runs
Curriculum learning entered large-scale reasoning training through reinforcement learning with verifiable rewards (RLVR): difficulty-ordered or difficulty-filtered problem schedules are now routine in systems built on GRPO and DAPO, and dozens of 2024-2026 papers report a gain from some…
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Journal article
Faithful to What? Four Instruments for Chain-of-Thought Faithfulness Disagree With Each Other, and the One Ground-Truth Check Run So Far Found Most of Them Near Chance
A chain-of-thought (CoT) trace is called "faithful" when it accurately represents the computation that produced the model's answer, as opposed to a plausible-sounding story invented after the fact (Jacovi and Goldberg, 2020). Whether published CoT traces meet this bar is disputed, and…
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Journal article
When Deliberation Hurts: Inverse Test-Time Scaling, Unfaithful Traces, and the Case Against a Unified System-2 in LLM Reasoning
The dominant frame for large reasoning models borrows a label from dual-process psychology: a fast, intuitive System 1 and a slower, deliberate System 2, with longer chains of thought read as more of the latter and therefore, on average, more reliable. A survey of reasoning LLMs states…
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Journal article
Who Pulls the Plug? Self-Report, External Authority, and the Limits of Self-Isolation in AI Control
A self-isolation proposal asks an AI system to notice that it may be compromised and to withdraw its own privileges. The AI control literature is built on the opposite premise: a model under evaluation may be actively subverting the protocol that watches it, so containment authority is…
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Journal article
The Emotional Intelligence Paradox in Large Language Models
The emergence of Large Language Models (LLMs) has revolutionized our understanding of artificial intelligence's capabilities in emotional processing. These models demonstrate remarkable proficiency in generating emotionally appropriate responses, yet this very capability presents us with…
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Journal article
An Intelligent Eye in the Sky: AI-Infused Drones for Autonomous High-Tech Security Operations
As security concerns continue to escalate globally, there is an increasing demand for highly responsive, intelligent surveillance systems capable of proactively managing and mitigating threats. This paper explores an innovative approach to security through the development of a…
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Journal article
Quantum Mirrors of the Mind: Breaking the Barriers Between Human Consciousness and Artificial Intelligence
In the nascent field of quantum consciousness computing, we present groundbreaking research that fundamentally transforms our understanding of both human consciousness and artificial intelligence. This paper introduces the Quantum Mirror Framework (QMF), a revolutionary approach that…
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Journal article
Intent Is Not a Property of the Record: Agent Memory Contamination, the Two Detector Families That Cover Disjoint Failures, and the Base Rate Nobody Has Measured
Persistent agent memory has acquired a security literature that models contamination as adversarial injection, and detectors are trained and evaluated on attacker-generated distributions. A separate empirical literature reports the same downstream damage with no attacker present, when an…
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Journal article
A Second Model Is Not a Second Opinion: Peer Verification, Correlated Failure, and the Independence Variable Nobody Reports
When a language model cannot reliably detect its own errors, the standard remedy is to have a second model check the first. That substitution is now the default across agent benchmarks, reward pipelines and safety evaluations, and it rests on an assumption that is rarely stated and that…
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Journal article
Ordering Is Not Resolution: What the Instruction Hierarchy Defines, What It Leaves Undefined, and the Same-Tier Conflicts Agent Benchmarks Do Not Separate
When a language-model agent receives instructions that conflict, the dominant remedy is a privilege ordering over sources: system above developer, developer above user, user above tool output. This paper argues that the ordering paradigm is well-defined for one class of conflict and…