Paper formalizing AI agent memory as a distinct data-management workload. Proposes GEM (Governed Evolving Memory) with four state-level operators (ingestion, revision, forgetting, retrieval) and six correctness conditions. Proves record-level systems cannot satisfy these conditions. Prototype MemState on property-graph backend.
Study on the cost of structured output constraints for small language models (< 3B). Tests on Qwen2.5-0.5B/1.5B and SmolLM2-1.7B show that enforcing JSON schema validity (61.5% → 100%) reduces answer accuracy (19.7% → 11.0%) and increases semantically invalid outputs (49.5% → 88.9%). Recommendation: report schema validity, answer accuracy, and semantic error rates separately.
Mechanistic analysis of LLM hallucinations on linearized structured knowledge (graphs, tables). Hallucinations stem from systematic internal dynamics: attention disproportionately concentrates on shortcut structural cues, feed-forward representations fail to ground provided knowledge, model reverts to parametric memory. Patterns generalize to multi-hop graphs and tabular data.
Unified Neural Scaling Law (UNSL) functional form models simultaneous variation of model parameters, training dataset size, training steps, inference steps, compute, and hyperparameters on performance. Validated across vision, language, math, and reinforcement learning with more accurate extrapolations than existing scaling laws.
AirCast-SR is an atmospheric super-resolution foundation model that downscales global AI weather forecasts from 28 km to 1 km horizontal resolution. Built on a 3D U-Net conditioned within a Latent Consistency Model diffusion framework, trained on GraphCast forecasts and NOAA data, it produces 67-hour forecasts with near-zero bias and demonstrates zero-shot global transferability to India and Germany.
JobBench evaluates 36 AI models (including Claude Opus at 45.9%) on 130 real professional tasks across 35 occupations. Unlike existing benchmarks focused on economic value, JobBench prioritizes workflows experts identify as high-priority for delegation, favoring human augmentation over replacement.
MedGuideX transforms clinical practice guideline (CPG) recommendations into executable decision logic to generate question-answering training data. Post-training a medical LLM on this data improves accuracy by 10.28% across four clinical reasoning benchmarks and produces physician-preferred rationales in faithfulness, validity, completeness, and clarity.
MechRL uses a PPO agent operating over 144 attention heads of GPT-2 small to automatically discover mechanistic circuits. Trained on induction and IOI tasks, the agent identifies causally relevant heads via zero-ablation and contrastive rewards, generalizing to docstring completion (96% of oracle with best-of-five planning).
TSFMAudit, first contamination auditing method for time series foundation models (TSFMs). Detects whether evaluation datasets were exposed during pretraining by analyzing fine-tuning adaptation dynamics: contaminated data exhibits unusually fast loss reduction. Evaluated on 6 TSFMs and 187 datasets.
ARBITER corrects majority vote failures in test-time sampling. Reasoning trajectories cluster into stable basins that aren't necessarily accurate. ARBITER uses hidden states and model-derived evidence to add conservative signals to consensus, recovering ~22% of oracle gap on Llama-3.1-8B MMLU-HS-Math (78%→82%).
GAC is an adaptive controller for hybrid SFT-RL post-training that dynamically adjusts mixing weights based on online estimates of gradient variance and disagreement between the two training signals. Tested on math, code, science, and logic benchmarks, GAC improves fixed baselines with less than 1% computational overhead.
arXiv paper reveals that models with statistically indistinguishable atomic knowledge fail systematically to chain them in multi-hop reasoning (>40 percentage point gap). Aggregate metrics mask this 'composition collapse'. Authors introduce a double-gate protocol decomposing post-training gains into three independent channels: atomic stability, residual composition, and critical depth.
Staged-Competence, a curriculum learning framework, improves robustness of DPO-based safety alignment. Across three model families, it reduces out-of-distribution harmful response rates by 16% and jailbreak attack success rates by 20%, while preserving general capabilities. The framework achieves baseline safety with 75% of training data.
Study of 432 experiments across 6 models (4 capability tiers) testing whether higher-capability models need less structural guidance. Results refute monotone relationship: Gemini 2.5 Flash performance drops 29-38pp with increased harness verbosity. Qwen3.5-122B (reasoning) achieves 91.7% VTSR with strict harness. Six-label failure taxonomy introduced.
FAB-Bench is an adaptive benchmarking framework for evaluating RAG systems in semiconductor manufacturing. It defines 6 diagnostic metrics (factual accuracy, contextual utilization, completeness, retrieval relevance, technical depth, reasoning consistency) across context windows of 4K-32K tokens. Benchmark of 200 query-answer pairs tested on 4 LLMs and 4 RAG frameworks.
LiveK12Bench is a dynamic multi-disciplinary benchmark evaluating reasoning capabilities of multimodal models on 2K+ real exam questions (Math, Physics, Chemistry, Biology). Tests reveal major performance degradation: GPT-5 drops from 79 to 53/100 under realistic exam constraints. Framework includes automated anti-contamination pipeline and end-to-end 'Mock Exam' evaluation scheme.
Stateful inference architecture for multi-agent tool calling with persistent KV cache across turns, reducing cost from O(n_t) to O(Δ_t). 2.1× speedup on 6-turn workflows, 4.2× on 35-turn median vs vLLM/SGLang.
CE-FedGNN is a federated framework for graph neural networks that reduces communication by infrequently exchanging aggregated node representations instead of per-round embeddings. A moving-average estimator handles cross-client dependencies and staleness. The framework provides privacy guarantees via metric-DP and achieves O(1/√T) convergence with O(T^3/4) communication complexity.
PAC-Bayesian framework for physics-informed machine learning (PIML) integrating partial differential equations. Provides high-probability generalisation guarantees with unbounded losses via multi-task perspective. Non-vacuous bounds validated on standard PDE benchmarks.
Study of adversarial online learning on hidden-convex losses (nonconvex losses becoming convex after reparameterization). Authors prove online gradient descent (OGD) achieves optimal Θ(√T) regret, improving prior O(T^2/3) result. They characterize necessary-and-sufficient Hessian compatibility condition and extend analysis to bandit feedback with O(T^3/4) regret.
Researchers test whether LLMs encode formal syntactic structures (Minimalist Program phase boundaries) invisible to Universal Dependencies. Across 13 models (4 families), 12/13 show a phase-count gradient, and 13/13 display an asymmetry predicted by phase-internal cohesion. Activation patching confirms these representations are causally active in 12/13 models.
OmniToM is a benchmark evaluating theory of mind in LLMs through explicit belief modeling. Built on 895 stories (22,343 annotated belief propositions), it tests extraction and labeling of mental states across 7 dimensions. Results show current LLMs struggle to transform narrative facts into actors' beliefs and shared mental states.
Verilog-Evolve is a feedback-driven framework for iterative Verilog refinement from LLM generation. The system evaluates candidates via functional simulation, Yosys synthesis, ABC timing proxy, and GEMM metrics, then evolves modular skills across tasks. Results on VerilogEval show improved functional success and downstream RTL quality.
MicroSpec reduces active vocabulary by 40x (under 3k tokens) during speculative decoding without additional training. The technique exploits temporal locality in language generation and integrates asynchronous GPU memory management. End-to-end speedup of 1.12-1.32x vs EAGLE-2.
Study on chain-of-thought (CoT) mechanisms at probe time. Authors show performance gains arise primarily from lexical activation and short-range token co-occurrence (2-3 tokens), not global logical derivation. Even word-shuffled rationales substantially outperform no-rationale baselines.
MobileExplorer accelerates on-device inference for mobile GUI agents through online exploration. The framework exploits VLM reasoning time to parallelly probe UI elements, recording exploration traces as structured memory. With a two-level rollback mechanism, it reduces reasoning steps and end-to-end latency by 23% on AndroidWorld.
QAM-W is a 2D quantization codec for LLM weights using Hadamard rotation and activation-aware scaling. Across 5 models (1.1B–13B), the activation-aware variant at ~5.5 bpw maintains ±0.4% BF16 perplexity, matching SmoothQuant W8A8 quality with 32% fewer weight bits. 2D coding outperforms polar coding by 2–15 pp.
Reasoning models (LRMs) jointly encode refusal in residual stream activations and chain-of-thought (CoT). On DeepSeek-R1-Distill-LLaMA-8B, activation steering reverses refusal in 39% of cases with fixed CoT, but 70% without CoT. Regenerating CoT under steering achieves 94% success, revealing refusal is distributed across activations and CoT.
SkillOpt formalizes markdown skill file optimization as trainable parameters via bounded edits (add/delete/replace) proposed by a frontier model and validated against a held-out test set. Best skills converge with 1–4 accepted edits from ~920 tokens. A skill optimized on Codex transfers to Claude Code (+59.7 SpreadsheetBench) without modification.
PAT, an adaptive tensor parallelism method, optimizes the generation stage in synchronous RLHF. It dynamically reconfigures parallelization during decoding to compensate for response-length skew. Implemented on SGLang/VeRL, PAT reduces generation latency by up to 34.6% on LLaMA3.1-8B and Qwen3-14B.
New UDS (Unlearning Depth Score) metric to evaluate whether knowledge is truly erased in LLMs. Via activation patching, UDS measures mechanistic depth of unlearning layer-by-layer. Evaluation on 150 models and 8 methods: UDS outperforms 20 existing metrics in faithfulness and robustness.
DRIVE is a dual-level skill modeling framework for web agents under continual learning. It separates experiences into reasoning skills (transferable task logic across websites) and interaction skills (executable page-specific operations). On WebArena, DRIVE achieves 52.8% task success rate, +7.3pp over skill-free baseline.
IRNO (Iterative Refinement Neural Operator) enhances neural operators with an iterative refinement module using fixed-point iteration theory. A progressive spectral loss explicitly targets high-frequency errors. Results: 56% improvement on turbulent flow, error reduction to 1.48-2.04% in high frequencies on Active Matter.
Algorithm to compute exact bounds on SHAP values for neural networks by leveraging neural network verification. Reduces exponential complexity and scales to orders of magnitude larger search spaces than existing exact methods.
Graph-in-Graph (GiG) integrates biological knowledge graphs into deep learning for clinical analysis with limited data. Tested on ~9,700 patients across 5 tasks (cancer detection, prostate diagnosis, pan-cancer classification), GiG outperforms existing methods with gains up to 49 macro-F1 points in limited-sample settings.
Interdomain Attention merges transformers and state space models via kernel methods: attention features are projected onto basis functions maintained by an SSM, enabling query-conditioned attention over fixed-size state. On FineWeb-Edu (125M–1.3B), outperforms softmax baselines at 1.3B on validation perplexity and commonsense tasks, with length-flat behavior up to 3.5× training context.
SteER is a framework for interactive deep research using LLMs. It introduces interpretable control points allowing users to correct course mid-process via cost-benefit formulation. Results: +22.80% alignment improvement vs baselines, preferred by human readers in 85%+ of pairwise judgments.
SEAL is a closed-loop co-evolution framework for tool-use LLM agents. It collects verifiable trajectories, diagnoses turn-level failures, and uses these signals to jointly adapt the learning environment and agent policy. With 400 training samples, SEAL achieves +8.25 to +26.25 point gains across three backbones and shows positive out-of-distribution transfer.
Found in Conversation (FiC) is a training framework where LLMs self-teach to close the multi-turn gap (Lost-in-Conversation). Via View-Asymmetric Self-Distillation, the model distills between single-turn (teacher) and multi-turn (student) views. Tested on Llama, Qwen, Phi, OLMo (3B-14B), FiC recovers 92-100% of single-turn performance.
EchoDistill introduces an alignment-based noisy-to-clean self-distillation framework to improve Audio LLM robustness against real-world noise. A noisy student is optimized via GRPO using a frozen clean-audio teacher as semantic reference. Results: +4.18% GSR improvement under strong noise vs strongest baseline, +3.02% Acc on Qwen-Omni.