LFRAG introduces a multimodal RAG system using block-level instead of page-level retrieval. A semantic-layout fusion encoder integrates local semantics with global context. On LFDocQA benchmark, LFRAG improves answer accuracy by 7.20% and reduces token consumption by 73.07%.
Systematic study of the full lifecycle of model-generated agent skills: extraction, consumption, and transfer. Evaluation framework spanning 5 agentic task domains. Findings: skills beneficial on average but exhibit non-trivial negative transfer; extractor/consumer performance independent of model scale. Introduction of meta-skill to improve quality and reduce negative transfer.
BOHM is a hierarchical attribution method for compound AI systems that extracts component contributions directly from routing weights without evaluating arbitrary subsets. Zero marginal cost, compatible with opaque third-party APIs. On 18 LLMs (880 LiveCodeBench problems), Kendall tau=0.928 vs SHAP tau=0.980 at 9,000x more evaluations.
QASC (Query-Adaptive Semantic Chunking) improves document segmentation for RAG by integrating user queries at chunking stage. Using cosine similarity scoring, contextual window expansion, and chunk-level aggregation, QASC achieves F1=0.85, a 18-27% relative improvement over fixed chunking and 8-12% over semantic/agentic methods on 100 technical documents and 200 queries.
GENSTRAT introduces a benchmark for evaluating strategic reasoning in LLMs using procedurally generated card games. Evaluation of 9 models (GPT-5, Claude, Gemini-3.1-Pro) across 36,000+ matches. Methodology decomposes competence across 6 axes and measures local volatility (jaggedness) to diagnose real-world deployments.
A-LEMS proposes Energy per Successful Goal (EpG) metric for agentic systems instead of per-inference energy. Across 8 task families, agentic workflows consume 4.33x more energy than linear execution (888.1 J vs 205.3 J). Overhead driven by orchestration, not compute.
Claude Code evaluated on CLEVER (Lean 4 benchmark) generates valid specifications for 98.8% of problems, certifies 87.5% of implementations, and achieves 98.1% success on end-to-end program generation and verification. Study reveals mismatch between current benchmark difficulty and modern agentic prover capabilities.
Co-ReAct integrates step-level rubrics to guide ReAct agents in multi-step search-intensive reasoning tasks. A rubric generator trained with GRPO optimizes list-wise Spearman rank-correlation against multi-judge expert consensus. Measured improvements on DeepResearchBench and SQA-CS-V2 across 8B/14B and frontier models.
Red-teaming study of 30+ open-source LLMs (10 families, 5 countries) measuring capacity to generate biased political content via jailbreaks. Findings: systematic asymmetries (left-leaning bias), Overton Window contraction with model size, substantial regional differences, variable jailbreak potency across model families.
ThriftAttention uses mixed precision (FP16/FP4) for long-context attention on Blackwell GPUs. By selecting 5% of critical query-key blocks in FP16 and computing remaining blocks in FP4, the method recovers 89.1% of FP16 performance while maintaining FP4 efficiency. Code released.
Cross-lingual red-teaming study of four MLLMs (Claude Sonnet 4.5, GPT-5, Pixtral Large, Qwen Omni) showing jailbreak vulnerability varies by language. Role-play attacks less effective in Mexican Spanish, visual attacks more effective. Safety rankings do not transfer across languages.
SciAtlas is a large-scale academic knowledge graph integrating 43M papers across 26 disciplines, 157M entities, and 3B triplets. It features a neuro-symbolic retrieval algorithm with tri-path collaborative recall and graph reranking to enhance semantic search and reduce inference costs for AI agents in automated scientific research.
EVE-Agent is a self-evolving agent that generates its own questions, answers, and verifiable evidence spans without human annotations. An evidence verifier rewards text spans based on their marginal contribution to correct answers. The training curriculum becomes auditable and trustworthy without external oracles.
RAS (Reflection-Augmented Scaling) improves Cypher query generation by leveraging database error messages through in-context learning. Across three Neo4j datasets and five code-specialized language models, RAS reduces Query Execution Error Rate by 41–50% (n=5), outperforming independent resampling (32–38%).
TPA (Think, Plan, Ask) is a multi-agent framework that guides LLMs to proactively select questioning strategies for assessing Social Language Disorder (SLD) traits in autism. Tested on 484 clinical episodes (ADOS-2), TPA achieves 82.1% SLD trait coverage vs 65.5% for clinicians, with superior diagnostic efficiency (AUCC: 0.628 vs 0.458).
PACE is a self-evolution framework for small language model agents (4B–14B parameters). It coordinates prompt refinement with control-logic updates via held-out validation, without requiring frontier models. Across 12 backbone–benchmark combinations, PACE improves vanilla SLM agents by +9.2% and single-mode evolution baselines by +5.4%.
An arXiv study reveals that Test-Time Training (TTT) creates security vulnerabilities. Researchers identify three threat models enabling safety filter bypass: with LoRA, attack success rates reach 95% and 93% respectively. Vulnerabilities transfer to production fine-tuning APIs.
New LIFT method for fine-tuning diffusion language models (DLMs). Analysis shows vanilla SFT ignores token learnability based on masking. LIFT aligns learning with diffusion steps: easy tokens when input is masked, hard tokens with more context. Up to 3x gains on AIME'24/25 vs SFT baselines.
Fast-dDrive is a block-diffusion VLA (Vision-Language-Action) model for autonomous driving. It combines bidirectional refinement within semantic units with strict causal ordering, handles structured JSON outputs, and achieves 12× throughput speedup with SGLang. On nuScenes, L2 error reduced to 0.32m (22% improvement), SOTA on WOD-E2E.
MemAudit is a post-hoc auditing framework to detect poisoned memories in LLM agents. It combines a counterfactual influence score and a memory consistency graph to identify malicious records injected through normal interactions. Evaluated against MINJA attack, it reduces success rates from 70% to 0% in QA and 83.3% to 0% in reasoning tasks.
PCSP, a single RL policy conditioned on frozen LLM embeddings, controls 300 NPCs with distinct personas. Achieves 17x above-chance zero-shot identification, ρ=0.73 semantic-behavioral alignment, 22x faster than LLM-as-policy baseline. Deployed in UE5 on 64 agents with low failure rate.
Tensor Cache introduces a two-level cache for transformers: sliding-window local attention (L1) plus fixed-size outer-product fast-weight memory (L2) storing evicted KV pairs as a matrix. A learned gate fuses outputs. Improves memory-quality tradeoff on long-context models.
On 1-3B models, CoT in arithmetic relies on a positional shortcut: the model simply copies the number in the final position before the answer delimiter, regardless of intermediate reasoning. This strategy accounts for 54-92 pp of accuracy on GSM8K. Replacing that number with an incorrect value collapses performance even with correct steps.
Study showing explicit reasoning (CoT) benefits only specific tasks. Authors propose EDRM, a training-free framework using early-stage entropy dynamics to adaptively route to CoT or direct inference. Across 15 benchmarks and 4 LLMs, EDRM achieves 41–55% token reduction while improving accuracy up to 4.7%.
Learned Relay Representations (Relay) enables Masked Diffusion Models to propagate latent information across denoising steps via a differentiable per-token channel trained with truncated BPTT. Applied to Fast-dLLM v2, it outperforms supervised finetuning on coding tasks and reduces inference latency by 32%.
Study of 16 language models (1.5B–72B parameters) showing representational convergence does not extend to reasoning processes. Models align more on collectively failed problems (CKA=0.897) than solved ones (CKA=0.830). Post-decision representations diverge sharply (CKA=0.274), and shared information exerts minimal causal influence (1.5–5.5% flip rate).
Audit of 11 long-context reasoning benchmarks finds none jointly control task position, filler content, and context length. Evaluation of 9 LLMs using Context Rot Evaluation (CRE) reveals sharp accuracy drops when target task moves from end to middle (e.g., Mimo-v2-Flash -88pp at 64K). Newer model releases show reduced positional vulnerability.
RMA is a multi-agent system for solving research-level mathematical problems. It decomposes proof solving into specialized modules (problem analysis, literature search, verification) coordinated by initializer, proposer, and verifier agents. On the First Proof benchmark (10 problems), RMA solves 8/10 and outperforms GPT-5.2R and Aletheia.
MaR (Metacognition-as-Reward) is an RL framework improving LLM reasoning via two dimensions: metacognitive knowledge (identifying task-relevant information) and metacognitive regulation (planning the reasoning process). Tested on 22 benchmarks, Qwen3.5-9B + MaR achieves up to 7.7% gain over base model and 11.0% over vanilla DAPO, surpassing GPT-OSS-120B on average.
SF-NorMuon, a schedule-free spectral optimizer, matches or exceeds tuned AdamW on 125M and 772M parameter language models without requiring a predefined learning-rate schedule. Theoretical proof of stationarity guarantee and identification of weight decay as essential for long-horizon stability.
Reproducible pipeline for building a Universal Dependencies parsing resource for Katharevousa Greek (post-junta parliamentary texts). Dataset of 1,697 annotated sentences, comparison of 6 architectures (spaCy, Stanza, XLM-R, mBERT, etc.). Best model (XLM-R): 0.5162 LAS vs 0.4183 baseline. Code and annotations released open-access.
Study of 20 commercial and open-source LLMs across 182 religious pairings. Models exhibit persistent asymmetries: they favor conversions to Catholicism, Bahá'í, Sikhism and discourage conversions to Atheism, Agnosticism, Jehovah's Witnesses. Grok 4.20 shows strongest asymmetries. Patterns reproducible across question phrasings.
Method to quantify uncertainty in language models by analyzing layer-wise representation trajectories. Eleven geometric features extracted from MLP updates outperform maximum softmax probability (MSP) by up to 21 AURC points, revealing where and how errors emerge across depth.
GEMQ introduces global expert-level mixed-precision quantization for MoE-LLMs. The method uses global linear-programming formulation to estimate expert importance and fine-tunes routers to adapt routing to quantized experts. Results: significant memory reduction and inference acceleration with minimal accuracy loss.
Sponsio introduces a deterministic contract layer for LLM agents in production. Operators declare invariants in YAML compiled to AST evaluated per tool call. ODCV-Bench benchmark (12 frontier LLMs × 80 trajectories): unguarded models violate in 11.5%-66.7% of runs; with Sponsio, 95.6% misalignment avoided on average.
Researchers from UMD, Google, and Meta use AutoTTS to let Claude Code independently discover control algorithms for AI reasoning. The discovered algorithm reduces compute by 70% versus standard self-consistency while matching accuracy. The search cost $40 and took 160 minutes.
Cohere released Command A+ (218B MoE, 25B active) on the 20th. MLX port for Apple Silicon in PR: cohere2_moe implementation with sigmoid routing, 128 top-8 experts, 3:1 sliding window. Validation on M3 Max (128GB): 22.9 tok/s generation, 57.6 tok/s prompt in BF16→Q8.
CPU benchmark of Needle (26M) vs Qwen3-0.6B on function calling: 50 queries across 5 difficulty tiers. Needle wins on accuracy (72% vs 56% tool_match) and latency (10.9s vs 47.9s). Needle fails on tool selection, Qwen3 on tag emission. Qwen3 dominates on multilingual queries (Hindi, French).
Apex-Testing, an agentic coding benchmark based on 65-70 real GitHub repos, updated to 95% with recent models. 70 tasks across 8 categories test AI agents on production codebases. ELO leaderboard, cost/time metrics and model comparisons available. Qwen 3.7 Max, Deepseek v4 and other models still being completed.
Contribution to exo: native multi-token prediction (MTP) support for Qwen 3.6 MLX models. Benchmarks on 27B (2x speedup at K=2/K=3) and 35B-A3B (1.16x at K=1). Exactness verified: identical token IDs to greedy path, speculative probability-ratio acceptance in sampling.