arXiv AI
Jun 9
Instrumented data for causal scientific machine learning
arXiv:2606. 07865v1 Announce Type: cross Abstract: Scientific machine learning is limited less by model size than by the data it is trained on.
LAWFUL: Law-Aligned Witness for Faithful Use of Latents
arXiv:2607. 28672v1 Announce Type: cross Abstract: When a neural network predicts a physical system accurately, has it learned the governing law as formal, structured knowledge, and if so, does the network's internal computation actually use that representation throughout the law's domain of validity?
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arXiv Computation and Language
Sep 17
Legal LLM Hallucination Should Be Evaluated as Failure of Legal Warrant
The paper argues that hallucinations by legal language models should be judged as failures of legal warrant rather than mere factual or citation errors. It defines claim-authority warrant as a context-sensitive relationship between a legal claim and applicable, current authority, and proposes that evaluating warrant can uncover failures missed by traditional accuracy or citation metrics. The authors outline a pilot study, benchmark specifications, and a research agenda to assess whether legal AI systems’ claims are properly licensed by law.
arXiv Computer Vision
Sep 4
VeriPhy: Agentic Physical Reasoning for World Model Evaluation and Refinement
VeriPhy is an auditable physical‑verification system that transforms a text prompt into typed physical obligations and a statically validated execution plan before any video frames are generated. During execution, it gates calls to frozen low‑level experts (segmentation, tracking, counting, depth, OCR, audio‑event detection, etc.) and records provenance‑carrying evidence for each action. The system maps these records to a three‑valued state—supported, contradicted, or unknown—providing traceable verdicts that can be used to refine generation models.
arXiv AI
Jul 28
WIRE: Profiling Witnessed Within-Policy Instruction Collisions in LLM Agents
arXiv:2605. 27784v2 Announce Type: replace Abstract: LLM agents are governed by long-lived prompt policies, where individually reasonable stand- ing rules can jointly govern the same pre- generation state.
arXiv AI
Aug 13
Agent Safety Should Be a Runtime Contract
arXiv:2608. 11274v1 Announce Type: cross Abstract: The dominant paradigm treats AI safety as a property to be instilled during model training via RLHF, DPO, or Constitutional AI.
arXiv AI
Oct 7
When Can World Models Recover Physical Laws?
arXiv:2610.06877v1 Announce Type: cross Abstract: Accurate prediction does not establish that a world model has recovered a physical law: distinct dynamics can generate identical records under the sa...
arXiv Machine Learning
Aug 7
SEAM: Global consistency beyond local accuracy in scientific machine learning
arXiv:2608. 05702v1 Announce Type: new Abstract: Scientific machine learning commonly validates models at the level of a subdomain, a benchmark split, or an explanation for one prediction.
arXiv AI
Sep 15
Calibrating Interpretability Instruments Before Trusting Their Verdicts
arXiv:2609.14754v1 Announce Type: cross Abstract: Causal claims about large language model (LLM) internals rest on measurements. Those might include a projection, a cosine, an ablation delta, or an i...
arXiv AI
Jun 4
Parthenon Law: A Self-Evolving Legal-Agent Framework
arXiv:2606. 04602v1 Announce Type: new Abstract: As agents grow more capable, legal-domain LLM agents promise to turn document-heavy matters into reviewable work products -- yet reliable deployment faces three obstacles: no large-scale evidence on how today's strongest model-and-harness combinations behave on end-to-end legal matters; no agent architecture adapted to the legal vertical, only general-purpose harnesses; and, in a setting that keeps shifting with new facts, authorities, and deadlines, no mechanism for systems to learn from their own outcomes.
arXiv AI
Jul 28
Confidently Wrong: Exception Chain Collapse in Frontier LLM Rule Evaluation
arXiv:2607. 23386v1 Announce Type: new Abstract: We document a failure class in frontier large language models -- exception chain collapse -- observed in eligibility evaluation under nested conditional rules of the form "A is required UNLESS B applies, UNLESS C overrides B".
arXiv Computation and Language
Nov 4
A Bounded Evaluation Protocol for Representational Systematicity
The paper introduces Bounded-RS, a protocol for testing whether neural networks are representationally systematic. It evaluates explanations by checking if the claimed representation is recoverable and causally used, then classifies the explanation into one of four states: Unsupported, Present-only, Causal-use, or Undetermined. Applied to a semantic‑parsing benchmark, the protocol finds that T5’s role‑filler Binding explanation is Causal‑use while its Geometry explanation is Unsupported, and it demonstrates high reliability across different configurations.
arXiv AI
6d ago
Certified Circuits: Stability Guarantees for Mechanistic Circuits
Certified Circuits introduces a framework that wraps any black‑box circuit discovery algorithm with randomized data subsampling to provide provable stability guarantees. By certifying that inclusion decisions over circuit components are invariant to bounded edit‑distance perturbations of the concept dataset, unstable components are abstained from, producing more compact and accurate mechanistic explanations. Across ResNet, ViT, and GPT‑2 on vision and language tasks, Certified Circuits achieves up to 56% higher accuracy and up to 80% fewer components, maintaining reliability where baseline methods degrade.