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180 records found · page 9 of 9
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Journal article
Formal Verification of Neural Network Safety Beyond Toy Examples: Three Distinct Gaps and a Specification-First Research Agenda
Formal verification of neural networks has matured into a real research area. Tools such as alpha-beta-CROWN, ERAN, and Marabou now reliably verify L-infinity robustness properties of ReLU networks at the ResNet scale, with the annual VNNCOMP competition documenting steady year-over-year…
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Journal article
Measured Against an Incomplete Answer Key: What Unknown Recall Certifies in Open-World Object Detection, Why Its Two Cost-Side Instruments Disagree, and the Separation That Was Published and Not Adopted
Open-world object detection asks a detector to put boxes on objects whose classes it was never trained on, and the field's headline number for that ability is unknown recall. This paper reconstructs where that metric came from and what it can certify. The founding paper of the task did…
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Journal article
Not Acting Is Not One Decision: Two Abstention Triggers in Tool-Using Agents, Why the Calibration and Permission Readings Cover Different Ones, and the Cost Term Neither Supplies
An agent that declines to send the email has made a decision that looks like the decision a language model makes when it declines to answer a question, and the resemblance has organised the 2026 literature into two camps. One reads not-acting as a calibration problem: the agent should…
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Journal article
Mechanistic Interpretability of In-Context Learning: A Survey of Known Circuits and Open Problems
In-context learning (ICL) — the ability of a transformer language model to acquire a new input-output mapping from a handful of demonstrations in its prompt, with no weight updates — remains the most striking and least understood capability of large language models. Mechanistic…
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Journal article
Three Things Called Budget Awareness: Observability, Forecasting and Allocation in LLM Agents, Why Every Published Allocation Gain Is Keyed to a Signal Measured After the Fact, and the Run-to-Run Variance No Forecast Is Scored Against
Two 2024-2026 literatures make claims about resource use in language-model agents that look incompatible. One reports that allocating test-time compute according to problem difficulty beats spending it uniformly, by margins up to a factor of four in compute at equal accuracy. The other…
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Journal article
A Failure to Reject Is Not a Finding: Closed-Set Accuracy, Specialized Open-Set Recognition, and the Semantic-Distance Parameter the Deflationary Debate Leaves Free
A widely cited result reports that the open-set performance of an image classifier is strongly correlated with its closed-set accuracy, and that a well-trained baseline scoring rule is competitive with specialized open-set machinery. The result is often read as showing that specialized…
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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
Cross-Lingual Hallucination Patterns in Indic Languages: A Typology-Aware Research Agenda for Dravidian and Indo-Aryan Comparison
Multilingual hallucination in large language models has begun to receive systematic attention. Recent work has measured aggregate hallucination rates across 19 to 30 languages (FACTSCORE-multilingual, MFAVA, Poly-FEVER), introduced multilingual benchmarks (HalluVerseM3, BHRAM-IL for some…
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Journal article
Causal Representation Learning from Observational Video: Identifiability Assumptions and the Gap Between Synthetic and Real-World Settings
Causal representation learning (CRL) — the recovery of latent causal variables and their causal graph from high-dimensional observations — has produced a substantial body of theoretical results in the past five years. CITRIS, CauCA, CRID, mechanismsparsity methods, and the recent…
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Journal article
Catastrophic Forgetting in Continual RLHF: A Measurement Framework for Round-Over-Round Capability Degradation
Reinforcement learning from human feedback (RLHF) is widely understood to incur an alignment tax: aligning a language model with human preferences can degrade capabilities the base model possessed. This phenomenon is well documented in single-round comparisons. What is not well…
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Journal article
Two Kinds of Missing: Underspecified Inputs Versus Unknown Answers, and Why One Abstention Policy Cannot Serve Both
A language model that declines to answer is scored the same way whether the reason is that the question has several readings and it picked the wrong one, or that the question has one reading and the model does not know the fact it names. AbstentionBench (Kirichenko et al., 2025) makes…
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Journal article
Consistency Is Not Correctness: What Internal Contradiction Detection Certifies, Why the Entailment Is Disjunctive, and Which Decisions It Leaves Unlicensed
Contradiction detection occupies an unusual position among proposals for making a language model check itself. Comparing two of a model's own outputs appears to need no external oracle, which makes it look like the one exception to the observation that working hallucination detectors sit…
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Journal article
Memory Architectures Beyond Attention: Disambiguating Four Memory Concepts and the Long-Context Reasoning Frontier
State-space models did not merely "open a door" beyond attention; they walked through it. Mamba, Mamba-2, Samba, Jamba, Granite 4, Hymba, and a growing family of hybrid attention-SSM architectures are now in production language models, demonstrating that linear-time alternatives to…
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Journal article
Neuromorphic Computing for On-Device LLM Inference: Why the Three-Layer Integration Gap Matters More Than the Algorithm Layer
Spiking neural networks (SNNs) on neuromorphic hardware are routinely proposed as the path to energy-efficient on-device inference for language models. The framing usually treats this as a single technical question. It is not. There are three distinct technical layers — SNN algorithms…
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Journal article
Beyond ASL: AI for Low-Resource Sign Languages A Research Agenda for Indian and African Contexts
There are over 300 sign languages in active use worldwide, serving an estimated 70 million Deaf signers. Despite this, the overwhelming majority of AI research on sign languages targets American Sign Language (ASL), with smaller but growing efforts on German, British, Chinese, and Indian…
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Journal article
Sheaf-Theoretic Semantics and Quantum Contextuality in Large Language Models: A Unified Categorical Framework for Understanding Semantic Coherence and Hallucination Phenomena
Abstract This paper introduces a novel theoretical framework for understanding semantic processing in Large Language Models through the lens of sheaf theory and quantum contextuality. We propose that the semantic structure of LLM-generated text can be rigorously modeled as a sheaf over…
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Journal article
A Conflict Is Constructed Before It Is Measured: Seven Design Choices That Set Which Way a Language Model Bends, and Why the Context-Memory Results Do Not Compare
The literature on how language models behave when retrieved context contradicts parametric memory contains a flat contradiction, and neither of its two surveys resolves it. One body of results reports over-reliance on memorised information; another reports that models are highly…
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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
Emergent Covert Signaling in Multi-Agent LLM Negotiation: A Conceptual Framework and Experimental Protocol
When multiple large-language-model agents negotiate, communicate, or compete, do they spontaneously develop covert signalling — channels of communication that human observers cannot decode? Recent work has established that such behaviour is possible: LLMs can be trained or pressured into…
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Journal article
Attribution Is Scored on a Finished Trace: What Failure-Attribution Benchmarks Measure, What Cascade Containment Would Need, and Why Neither Result Bounds the Other
A family of recent proposals aims to stop errors from spreading through LLM-based multi-agent systems: genealogy-graph governance over message dependencies, cross-channel causal monitoring, propagation-aware remediation of contaminated state, infection-aware safeguarding, and corrector…