The Archive

Proposes Memory-Enhanced Test-Time Adaptation (METTA) for meta-RL using dual-timescale memory to improve fast task adaptation from limited interactions.

Introduces EpiSEM-Bench, executable benchmark with 300 task pairs measuring unsafe execution when tool-using LLM agents face missing safety-relevant facts.

Proposes low-precision storage for n-gram embedding tables in language models, targeting 196B-parameter tables in DeepSeek-V4.1-Flash without performance loss.

Identifies and fixes two numerical stability bugs in softmax/cross-entropy implementations: Softmax Collapse and Softmax Cancellation.

CausalCoT enforces causal transparency in chain-of-thought reasoning by decomposing problems into DAGs of sub-questions, preventing post-hoc justifications.

LLM agent debugs FineWeb2 Slovak corpus via tool-use auditing, finding 1.4M machine-generated documents (5.32%) for $2.21-$2.84 per run.

BeNice addresses looped architecture inference memory via KV-cache sharing across recurrences, investigating shareability mechanisms.

RLVR checkpoint analysis on OLMo-3 empirically tests whether reinforcement learning induces instrumental convergence (power-seeking, deception).

Bilingual LMs capture L1-driven cross-linguistic influence in L2 reading, validated on 12-language eye-tracking cohorts.

OP-Print demonstrates membership inference attacks against on-policy self-distillation, exposing privacy risks from coupled training views.

Modal CEO Bernhardsson analyzes why AI compute infrastructure defaults to rectangular resource allocation, examining elastic GPUs and sandbox economics for agent deployment.

Polynomial-time algorithms for learning discrete distributions under person-level differential privacy match information-theoretic optimality.

Evidence-aware evaluation protocol for clinical RAG system on breast lesion corpus tests retrieval chunk size, overlap, and reranking.

CRANE enables post-hoc counterfactual explanations for multimodal classifiers via nearest-neighbor retrieval, agnostic to architecture.

Simulated control evaluations (SCEs) use LM-simulated tool responses to broaden safety testing beyond purpose-built control environments.

Diffusion models for inverse problems fail on ambiguous measurements; proposes prior-consistent DAPS to avoid measurement-guidance favoring non-prior modes.

VERA dashboard operationalizes EU AI Act compliance for LLM deployments, mapping 18 technical requirements to measurable benchmarks for non-engineer stakeholders.

Evaluates four open LLMs (8-27B) on Spanish MIR medical exam for uncertainty calibration, reasoning soundness, and multimodal grounding beyond accuracy.

Action learning recovers stochastic control policies from trajectory data, enabling unified objective for reasoning about system dynamics and optimization.

The Nintendo Switch 2 has apparently been jailbroken, and a modder has already shown the console running a custom version of Mario Kart World featuring new playable characters like Cinnamoroll, the red Angry Bird, Peter Griffin, and Steve from Minecraft. i jailbroke the nintendo switch 2 so i modded cinnamoroll, peter griffin, hatsune miku, and more into Mario Kart World, a switch 2 exclusive now watch my friends and i get cooked by bots learn more and get the exploit https://t.co/ykjgyfrFRp pic.twitter.com/BOcFhejRz1 - pepsipu (@pepsipu) October 22, 2026 OpenAI's Codex helped make this "ns2j...

Empirical study: users defer to confident but wrong LLMs over correct information; demonstrates trust calibration collapse independent of model sycophancy.

Ablates learned unmasking policies in masked diffusion LMs; identifies label source, policy inputs, and capacity as key determinants of speed-accuracy tradeoff.

TTR-Compressor uses test-time regression for sequence compression in LLMs; learns end-to-end via backprop, computes coefficients in closed form at inference.

Quantifies social-media exposure via LLM-inferred user profiles; measures accuracy and distance to social engineering attacks post-API landscape.

Identifies token collapse pathology in graph soft prompting (G-Retriever) across three benchmarks and model families; graph tokens lose discriminative signal.

Advantage-Based On-Policy Distillation weights teacher actions by counterfactual student advantage; outperforms standard OPD by distinguishing preference from utility.

Diffusion language models improve adversarial prompt attacks on black-box LLMs by generating natural, harder-to-detect malicious inputs via learned text decoding.

Framework for auditability of LLM-generated metadata in enterprises, tracking human stewardship decisions to prevent error propagation across systems.

Algorithmic stability framework decomposes catastrophic forgetting in continual learning into intra-task and cross-task components without prescribing architecture.

Privacy auditing method for deployed models without retraining, using score transport to handle distribution shift in membership inference attacks.