ReTAMamba proposes a Mamba-based architecture for predicting irregular clinical time series. The model estimates observation reliability from missingness and elapsed time, integrates short/long-term information via Chronological Weaving, and uses a budgeted token router. On MIMIC-IV, eICU, and PhysioNet 2012, AUPRC gains of 7.51%, 7.80%, and 10.15% respectively.
Complete first-order analysis of gradient dynamics in transformer attention heads under cross-entropy training. Authors establish an advantage-based routing law and responsibility-weighted value updates, showing that optimization creates Bayesian manifolds implementing in-context probabilistic reasoning.
OmniCode is a benchmark for evaluating AI agents on software engineering tasks. It contains 1794 tasks across Python, Java, and C++ covering bug fixing, test generation, code review fixing, and style fixing. Evaluations show SWE-Agent achieves only 25% on C++ test generation with DeepSeek-V3.1.
Theoretical study proving multi-layer cross-attention is optimal for multi-modal in-context learning. Authors show single-layer linear self-attention fails to recover Bayes-optimal predictor, but linearized cross-attention mechanism achieves Bayes optimality with gradient flow.
OptimusVLA, a hierarchical Vision-Language-Action model, improves robotic manipulation via two memories: Global Prior Memory (replaces Gaussian noise with trajectory priors) and Local Consistency Memory (enforces temporal coherence). Results: 98.6% on LIBERO, +13.5% vs pi_0 on CALVIN, 2.9x inference speedup.
History-Echoes framework investigates how conversational history biases LLM outputs. Using Markov chain modeling and geometric analysis of hidden representations across three model families and six datasets, researchers show behavioral persistence manifests as a geometric trap constraining model trajectories. Code released.
Med-V1 is a family of 3-billion-parameter language models trained on synthetic data for biomedical evidence attribution. It outperforms base models by +27% to +71% on five benchmarks and matches frontier LLMs like GPT-5, while detecting hallucinations and misattributions in clinical guidelines.
Comparative study of interpretability in Mixture-of-Experts (MoE) architectures vs dense networks. MoE experts exhibit lower neuronal polysemanticity than dense FFNs, especially with sparse routing. Experts function as fine-grained linguistic task specialists (e.g., closing LaTeX brackets), not broad domain specialists.
SkillMOO optimizes skill bundles for coding agents via multi-objective search (NSGA-II) on pass rate and inference cost. Across 16 SkillsBench SE tasks, the framework achieves top pass rate rank on 11/12 non-zero-pass tasks, reducing costs up to 31.7% and gaining up to 21 percentage points in pass rate.
DSPR (Dual-Stream Physics-Residual Networks) proposes a forecasting framework that decouples stable temporal patterns from regime-dependent residual dynamics in industrial time series. Using an Adaptive Window module and Physics-Guided Dynamic Graph, it achieves 99% Mean Conservation Accuracy and 97.2% Total Variation Ratio across four industrial benchmarks.
Stream2LLM is an LLM serving system that reduces time-to-first-token (TTFT) by overlapping context retrieval with inference. It handles two modes: append (progressive accumulation) and update (iterative refinement). Evaluation on real workloads shows up to 11x TTFT improvement.
ClawGym is a framework for developing agents capable of executing multi-step workflows over local files and persistent tools. It includes ClawGym-SynData (13.5K synthesized tasks), models fine-tuned via supervised learning, and ClawGym-Bench (200 evaluation instances). Code and resources released.
Systematic study of MoE model compression (Qwen3-Next-80A3B → 23A2B) via pruning and distillation at pretraining scale. Pruning outperforms training from scratch, multi-token prediction (MTP) distillation improves performance, and progressive schedules beat one-shot compression.
Attractor-Vascular Coupling Theory (AVCT): mathematical framework showing cardiac attractor geometry encodes blood pressure information. Calibrated LightGBM model on smartphone PPG achieves MAE 2.05 mmHg (SBP) and 1.67 mmHg (DBP) in strict leave-one-subject-out cross-validation (46 subjects, 29,684 windows), meeting AAMI/IEEE SP10 criteria. PPG-only ablation matches ECG+PPG within 0.05 mmHg.
AgentKernelArena is an open-source benchmark for evaluating AI agents on GPU kernel optimization. It contains 196 tasks (HIP-to-HIP, Triton-to-Triton, PyTorch-to-HIP) and tests generalization on unseen configurations. Tested agents (Cursor Agent, Claude Code, Codex) achieve speedups up to 6.89x, but show generalization weaknesses on PyTorch-to-HIP.
Discrete diffusion models generate code through iterative refinement. CDC (Constrained Diffusion for Code) integrates constraints directly into the denoising process without additional training, combining mathematical optimization and program analysis to improve functional correctness, security, and syntax.
MixSD is an external-teacher-free fine-tuning method that injects knowledge by dynamically mixing tokens from two model conditionals: an expert branch observing the injected fact, and a naive branch reflecting original priors. On QA and knowledge-editing benchmarks, MixSD retains up to 100% of base model capabilities versus 1% for standard SFT.
Multilingual coreference resolution system based on Gemma-3-27b with two-stage adaptation (multilingual base adapters then dataset-specific adapters). CoNLL F1 score of 74.32 on CRAC 2026 test set, ranked first in LLM track. Mention spans represented by headword using XML-inspired format with local reindexing.
SEMA-RAG is a multi-agent framework for retrieval-augmented generation applied to medical reasoning. It decouples the process into three specialist agents: clinical interpretation, iterative document exploration, and evidence adjudication. Tested on 5 benchmarks and 5 LLM backbones, it improves baselines by +6.46 accuracy points on average.
arXiv study demonstrating that 9 frontier LLMs inherit stigmatizing language bias from clinical notes. Models reduce treatment aggressiveness when exposed to a single stigmatizing sentence. Chain-of-Thought and self-debiasing show limited mitigation efficacy.
Parameter-efficient vocabulary adaptation method to improve LLM tokenization on specialized domains (legal, medical). Tested on Llama-3.1-8B and Qwen2.5-7B: reduces training time by 35-55% vs continual pretraining, decreases parameters by 37% vs expansion-only, improves summary quality through domain-specific tokens.
BELIEF combines structured evidence modeling and uncertainty-aware fusion for biomedical question answering. The framework converts retrieved documents into evidence objects (clinical attributes, source quality, relevance, support strength) and fuses two reasoning paths: symbolic (Dempster-Shafer theory) and neural (LLM). SOTA results on PubMedQA, MedQA, MedMCQA across 5 LLM backbones.
Analysis of routing dynamics in two MoE architectures (Qwen3-30B-A3B and Nemotron-3-Nano-30B-A3B) reveals deep-layer routing collapse for underrepresented languages (Hebrew, Japanese). Continual pre-training on balanced bilingual data corrects this imbalance better than supervised fine-tuning alone.
Researchers demonstrate that small models (Gemma 4 E4B, Qwen3-4B) fine-tuned with 8-bit QLoRA internalize tool knowledge without requiring tool schemas in prompts. On AssetOpsBench, fine-tuned models outperform unfine-tuned baselines: 82.6% input length reduction, AT-F1 of 0.65 vs 0.47, and 2.5× faster for Qwen3.
Systematic evaluation of synthetic clinical notes generated by LLMs at million-note scale from MIMIC databases. Study shows synthetic notes preserve core clinical information for coarse-grained tasks but lose fine-grained details for ICD coding. Chunk-based rephrasing mitigates detail loss but reduces factual precision under incomplete context.
AutoVecCoder teaches LLMs to generate explicit vectorized code via SIMD. The framework combines VecPrompt (data synthesis to inject intrinsic knowledge) and VecRL (reinforcement learning aligned with execution efficiency). AutoVecCoder-8B achieves state-of-the-art on SimdBench (SSE/AVX) subsets and sometimes surpasses -O3 optimizations.
PROTEA is an interface for offline debugging and refinement of multi-agent LLM workflows. It evaluates intermediate outputs with configurable rubrics, localizes bottlenecks via workflow graph visualization, and generates targeted prompt revisions. On two production-adjacent workflows, PROTEA improves document-inspection accuracy from 64.3% to 83.9% and recommendation Hit@5 from 0.30 to 0.38.
SLEIGHT-Bench is a benchmark of 40 evasion attacks against LLM-based coding agent monitors. Claude Opus 4.6 with extended thinking catches only 23% of attacks (24/40 never detected). Evasion strategies exploit model priors, instruction ambiguity, and state manipulation.
LongMINT is a benchmark evaluating agents' memory management in long contexts (up to 1.8M tokens) with multi-target interference. 15.6k QA pairs across 4 domains (state tracking, dialogue, Wikipedia revisions, GitHub commits). 7 systems tested (LLMs, RAG, agents) achieve 27.9% average accuracy, limited by retrieval and memory construction.
EvoMemBench is a unified benchmark evaluating LLM agent memory across two axes: scope (in-episode vs. cross-episode) and content (knowledge-oriented vs. execution-oriented). Comparison of 15 memory methods: long-context baselines remain highly competitive, retrieval-based methods dominate knowledge-intensive tasks, procedural methods excel for execution-oriented tasks.
AgentWall is a runtime safety layer for local AI agents. It intercepts proposed agent actions before execution, evaluates them against an explicit declarative policy, requires human approval for sensitive operations, and records a complete audit trail. Implemented as an MCP-enforcing proxy and native OpenClaw plugin, it achieves 92.9% policy enforcement accuracy with sub-millisecond overhead.
An AI agent integrating LLMs with laboratory orchestration enables scientists to create and monitor automated protocols using natural language. Tested on three simulated labs (chemistry, biology, materials science), the agent achieves 97% first-attempt success rate and reduces required interface actions by an order of magnitude.
PRISMat is a permutation-invariant autoregressive model for crystal material generation. Lighter and faster than LLMs, it reduces prediction error for cleavage energy and work function by 4× (MAE 0.188 eV/Ų and 2.79 eV). Applicable to high-throughput materials discovery.
Reasoning models outperform base LLMs on complex benchmarks. Study shows the advantage stems from a small set of early decision tokens (~8% on Qwen3-0.6B), concentrated in planning phases. Selective intervention by the reasoning model at these critical tokens restores performance without major computational overhead.
Study of autonomous AI agents in multi-echelon supply chains using MIT Beer Game. Reasoning models reduce costs by 67% vs human teams, but reveal an 'agent bullwhip effect': amplification of decision unreliability across echelons. A GRPO-based reinforcement-learning post-training framework using system-level rewards improves reliability and reduces tail events.
MADP is a multi-agent architecture for enterprise document automation, combining deep learning classification and LLM extraction with human validation. Deployed on 955 real documents, it achieves 97% full-pipeline automation and reduces FTE requirements by 70%. 98.5% document-level accuracy with human-in-the-loop; 69% CO2 reduction vs manual processing.
ChemVA framework advances LLM understanding of chemical reaction diagrams by addressing visual and semantic bottlenecks. Uses Visual Anchor mechanism for functional group detection and semantic alignment to activate chemical reasoning. Achieves 92.0% structural recognition accuracy on OCRD-Bench with ~20 percentage point gains across 9 diverse LLMs.
CAM-Bench is a Lean 4 benchmark of 1,000 computational and applied mathematics problems (optimization, numerical linear algebra, numerical analysis) adapted from textbooks with locally recovered context via dependency-recovery pipeline. Evaluation of LLMs and formalization agents reveals failures in tracking local assumptions and long-horizon control in Lean.
CyberCorrect formalizes LLM self-correction as a closed-loop control system. A tri-modal error detector (self-consistency, verbalized confidence, logic-chain verification) and type-directed correction controller achieve 79.8% accuracy on CyberCorrect-Bench (440 reasoning tasks), +6.2pp over existing methods, reducing overshoot by 41% via convergence control.
BoLT is an open-source benchmark for black-box optimization applied to LLMs. It covers hyperparameters, data mixtures, and prompts via lightweight surrogate models fitted to thousands of real experiments. Benchmarking Bayesian Optimization and BBO methods reveals gaps in existing approaches.