MERIT, a multimodal pretraining framework, combines masked ECG modeling with ECG–text contrastive alignment to learn cardiac representations. On PTB-XL: +3% F1 (All) and +5% F1 (SubClass), +2.66% AUC zero-shot. Also improves clinical text generation with LLMs.
GenSBI is an open-source JAX library for simulation-based inference (SBI). It implements flow matching, score matching, and denoising diffusion with three transformer architectures (SimFormer, Flux1, Flux1Joint). Validation on SBIBM benchmarks: C2ST scores of 0.50-0.56 (ideal=0.50).
BioELX is a two-stage cross-lingual biomedical entity linking system requiring no annotated training data. It enriches SapBERT with Wikidata-derived multilingual aliases and uses an LLM for context-aware disambiguation. On five benchmarks, it achieves +19.2 Recall@1 on XL-BEL, with major gains for low-resource languages (Turkish +21.6, Korean +22.1, Thai +30.8).
StoryMI is a multi-agent LLM framework for controllable motivational interviewing (MI) dialogue generation. Client profiles from questionnaires are expanded into narrative stories. Therapist and client agents generate MI-coded utterances, coordinated by an interaction agent. Evaluation on 6K simulated dialogues covering 12 MI codes and 13 symptom domains.
RAG-Coding is a multi-agent method orchestrating 4 LLMs for automated ICD-10-CM coding. It grounds decisions in external sources (official tabular, guidelines) and improves accuracy by 8-13% micro-F1 on MDACE. Authors release MDACE-2025 with expert annotations aligned to 2025 guidelines.
TritonMoE: pure Triton MoE kernel for portable NVIDIA/AMD inference without vendor-specific code. Fused gate+up GEMM reduces memory traffic by 35%. Achieves 89-131% of Megablocks throughput (batch ≤512 tokens) on A100, same kernel runs on MI300X. Limitations: degrades at 2048+ tokens and with 64+ experts.
Complete Usenet corpus (1980–2013) released for local fine-tuning: 103.1B tokens, 408M posts, zero AI contamination. Pre-SEO, pre-algorithm internet writing across 33 years. Organized by domain hierarchies (comp.*, sci.*, rec.*). Free samples available, full corpus under license.
Comprehensive benchmark of 38 KV quantization pairs on Qwen 3.6 27B with 64k-128k context. Q5_0 and Q5_1 underrated, Q8_0/Q4_* overrated. Recommendation: Q8_0/Q6_0 or Q8_0/Q5_1 for high-end, Q6_0/Q5_0 for balance, Q5_0/Q5_0 for tight VRAM.
Fused MoE dispatch kernel written in pure Triton (no CUDA) achieves 89-131% of Megablocks performance on A100. Fuses gate+up projections to cut 35% memory traffic. Runs on AMD MI300X with zero code changes. Limitations: degraded performance beyond 2048 tokens and with 64+ experts.
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.
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.
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.
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.
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.
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%).
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.
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.
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.
MemFail is a diagnostic benchmark isolating failure modes of modern LLM memory systems. Authors formalize these systems as composition of three operations (summarization, storage, retrieval) and construct five adversarial datasets to test each. Evaluation of four SOTA systems reveals architectural tradeoffs.
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.
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.
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.
NestedKV compresses KV cache for long-context models without training. The method maintains multi-scale key anchors (global, block-level, sliding-window), scores tokens by multi-time-scale cosine anomaly, and combines rankings with head-adaptive mixing and surprise-gated routing. Improvements up to 19.10 points on RULER and 19.29 on LongBench vs KeyDiff (Qwen3-4B, r=0.75).
Controlled study on path history length in knowledge-graph QA with LLMs. Bounded Path Context (BPC) limits exposed history (last K hops) while maintaining full path in symbolic memory. On WebQSP and CWQ with Qwen3.5-9B-AWQ: K=1 achieves 0.487 F1 (vs 0.472 full history) with 9.7% fewer tokens.
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.
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.
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).
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.
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.
Autoregressive video diffusion models use quantized KV caches to reduce memory, but quantization creates an attention bias (Jensen bias) that degrades quality. Authors propose a per-attention-score correction computed from quantization step sizes, recovering quality lost with INT2 quantization while using 50% less memory than INT4.
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.
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.
GEM (Geometric Entropy Mixing) reformulates LLM data curation as a variational problem on the hypersphere to prevent cluster collapse. Uses provable MM algorithm and teacher-student distillation for web-scale scaling. Improves downstream accuracy by up to 1.2% on 1.1B models integrated with DoReMi and RegMix.
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.
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.
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.
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.
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.
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.