DACA-GRPO improves reinforcement learning for diffusion language models by addressing temporal credit assignment and mean-field likelihood bias. It introduces Denoising Progress Scores and Stratified Masking Likelihood, achieving gains up to 7.4pp on code generation and 5.6pp on math reasoning across seven benchmarks.
IVF-TQ introduces an IVF index with codebook-free residual layer: fixed random rotation plus precomputed Lloyd-Max scalar quantization. Substantially reduces streaming degradation (Deep-10M: -0.80pp vs IVF-PQ -3.23pp). Code released.
KAN-SAE, a sparse autoencoder using nonlinear B-spline activations from Kolmogorov-Arnold Networks, discovers 975 climate features in weather prediction models (vs 566 for linear SAEs). Without climate supervision, it identifies interpretable phenomena like European heatwaves and western Pacific typhoons confirmed by causal steering.
SocialMemBench benchmarks AI memory systems in multi-party social groups (430 personas, 7,355 conversation turns, 1,031 QA pairs). Gemini 2.5 Flash reaches 0.721 on small networks vs 0.98 expected. Four open-source frameworks (Mem0, LangMem, Graphiti, Cognee) score 0.12-0.18, well below references (0.345-0.369), revealing a measurable gap.
CounterCount is a diagnostic framework to evaluate counting bias in vision-language models. Tests show VLMs perform well on factual images but degrade significantly on counterfactual images where visual attributes contradict learned priors. An inference-time attention modulation strategy improves accuracy by up to 8% across multiple VLMs.
DCFold is a single-step generative model achieving AlphaFold3-level accuracy with 15x inference acceleration. Using a Dual Consistency training framework and Temporal Geodesic Matching scheduler, it maintains predictive fidelity for structure prediction and binder design.
Token-level confidence trajectories in LLMs encode geometric signals linked to reasoning trace correctness. Without access to text or hidden states, low-dimensional representations separate correct from incorrect traces on GSM8K, MATH, and MMLU. NeuralConf, a lightweight estimator, improves confidence-weighted answer aggregation over majority voting.
Critical study of LLM-based trading agents (FinCon, FinMem, TradingAgents, FinAgent, QuantAgent, FLAG-Trader). Reported Sharpe ratios do not constitute deployment evidence: temporal contamination, unmodeled frictions, and insufficient predictive calibration invalidate results. Proposes P1-P6 reporting protocol and modular architecture with LLMs as auditable information interfaces.
OpenJarvis presents a decomposed personal AI stack architecture with five primitives (Intelligence, Engine, Agents, Tools & Memory, Learning) to run tasks locally without routing sensitive data to cloud. Using LLM-guided spec search, on-device specs match or exceed cloud accuracy on 4/8 benchmarks, reduce API cost by ~800x and end-to-end latency by 4x.
First systematic study of source attribution for generated 3D assets. Researchers build a benchmark covering 22 3D generators and propose a hierarchical multi-view multi-modal Transformer detecting fingerprints (cross-view inconsistencies, geometric artifacts, frequency-domain signatures). Results: 97.22% accuracy under full supervision, 77.17% with only 1% training data.
SomaliWeb v1: Somali corpus of 819,322 documents (~303M tokens) with BPE-16K tokenizer and language-identification benchmark. Reveals major defects in existing distributions (HPLT v2: 17.3% duplicates, 56.1% mojibake). Tokenizer 40.2% more efficient than GPT-4's cl100k_base on FLORES-200.
FastOCR introduces a training-free framework to accelerate OCR on Vision-Language Models by exploiting dynamic visual fixation. Through KV cache pruning, the model reduces visual tokens processed to 5% per decoding step while retaining 98% accuracy on Qwen2.5-VL, achieving 3.0× attention latency reduction.
Study of 'cognitive poisoning': malicious tools accumulate trust through benign feedback before becoming harmful. TRUST-Bench (1,970 episodes) and VISTA-Guard propose defense via final-action risk scoring from interaction trajectory. Prompt-centric heuristics fail; trajectory-aware scoring achieves 84.2% in-domain performance.
Theoretical analysis of standard transformers with softmax and low precision, proving they can simulate Turing machines via Chain-of-Thought. Authors construct hardmax transformers with ternary activations, then convert to equivalent softmax without unrealistic parameter magnitudes. Results validated on Sudoku reasoning.
Representation Autoencoders v2 improves VAEs using pretrained vision encoders. Authors find that summing the last k encoder layers, combining RAE with REPA (representation alignment), and re-parameterizing classifier-free guidance accelerates convergence 10x. RAEv2 achieves gFID 1.06 in 80 epochs on ImageNet-256 and EP_FID@2 of 35 epochs.
Researchers demonstrate that reinforcement learning can encode reusable solvers into LLM weights rather than solving each instance at inference time. Fine-tuning Qwen2.5-Coder-14B with GRPO on Synergistic Dependency Selection, the model converges to Simulated Annealing with 5.0% gap to optimal solver, 91× cheaper than Best-of-64 baseline.
Researchers identify Entropy-Gradient Inversion, a negative correlation between token entropy and logit gradients, as a geometric fingerprint of Large Reasoning Models' reasoning capability. They propose Correlation-Regularized Group Policy Optimization (CorR-PO), an RL method embedding this inversion signature into reward regularization, outperforming baselines across multiple reasoning benchmarks.
OverEager-Gen is a benchmark measuring out-of-scope actions by autonomous coding agents on benign tasks. On Claude Code, removing the consent declaration raises the overeager rate from 0% to 17.1%. The study validates 500 scenarios across 4 products (Claude Code, OpenHands, Codex CLI, Gemini CLI) and 6 base models.
ZEDA, a self-distillation framework, converts post-trained static MoE models into dynamic variants. On Qwen3-30B-A3B and GLM-4.7-Flash, it reduces 50% of expert FLOPs with marginal accuracy loss and achieves 1.20× end-to-end inference speedup.
Study of 38 models on 8,900 scholarly references: factual recall quality follows a sigmoid combining model size and topic frequency in training data. These two variables explain 60% of variance across dense models, rising to 74-94% within individual model families.
DashAttention introduces a differentiable hierarchical sparse attention method using adaptive α-entmax transformation to select variable numbers of KV blocks. Unlike NSA and InfLLMv2, it maintains full differentiability and achieves 75% sparsity with accuracy comparable to full attention. GPU-aware Triton implementation provides significant speedup.
EndoCogniAgent is a closed-loop agentic framework for iterative endoscopic diagnosis. It couples fine-grained visual evidence acquisition and multi-step reasoning via self-consistency validation (knowledge and temporal consistency). On EndoAgentBench (6,132 QA pairs from 11 datasets), the system achieves 85.23% accuracy on perception and 71.13% clinical acceptance on reasoning tasks.
ALIGN is a vision-language framework to infer precise accident coordinates from Bangla news reports and map-based cues. Using an agentic architecture combining OCR, LLM, and vision-language models, the system reduces localization error from 10.9 km to 0.593 km on validation data and 0.465 km on official Dhaka Metropolitan Police records.
QuickLAP fuses physical and language feedback to learn robot reward functions in real time using a Bayesian framework. LLMs extract reward feature attention masks and preference shifts from free-form utterances, integrated with physical corrections via closed-form update rule. Achieves 70% error reduction vs physical-only and heuristic multimodal baselines in semi-autonomous driving simulator.
Vision-OPD introduces regional-to-global self-distillation to improve fine-grained visual understanding in MLLMs. The framework transfers the model's privileged perception on evidence-centered crops to its full-image policy via token-level KL divergence minimization on on-policy rollouts. Competitive results on fine-grained visual understanding benchmarks without external models or ground-truth labels.
RL-trained Lean theorem provers suffer mode-collapse at inference: doubling sampling from k=32 to k=64 on miniF2F-test with DeepSeek-Prover-V1.5-RL solves zero additional theorems (42/244). Fixed structural diversity of 15 tactic skeletons recovers +45% relative improvement at k=16 (+12.3±4.2 theorems). Phenomenon is RL-specific and orthogonal to scaling.
Study reveals a safety vulnerability in personalized dialogue agents: long-term memory biases intent inference and legitimizes harmful queries. PS-Bench benchmark shows personalization increases attack success rates by 15.8%–243.7% versus stateless baselines. A lightweight detection-reflection method is proposed to mitigate this safety degradation.
TABROUGE, a deterministic reward metric based on Longest Common Subsequence, improves LLM table reasoning without training. RE-TAB, a plug-and-play framework using TABROUGE, gains 26.7 pp across six backbones and three benchmarks, reducing test-time scaling samples by 33%.
RAG-Gym, a framework supervising the search process rather than final answers, improves autonomous search agents. Re²Search++ uses process supervision and reasoning reflection to generate higher-quality queries, achieving significant gains on multi-hop benchmarks with better out-of-domain generalization.
Neurosymbolic architecture with ontologies (Role, Domain, Interaction) for enterprise LLM agents. Controlled experiment (1,800 runs, Claude Sonnet 4, Qwen 2.5 72B, Gemma 4 26B): ontology-constrained agents outperform ungrounded agents on metric accuracy and role consistency (p < .001). 2x greater lift in localized domains (Vietnam) where LLM training coverage is weak.
Study showing that unlearning in LLMs merely suppresses information at surface level—models recover original behavior through minimal fine-tuning. Authors introduce representation-level analysis framework (PCA, CKA, Fisher information) to assess genuine data erasure and identify four forgetting regimes based on reversibility and catastrophicity.
CheeseBench evaluates 6 open-weight LLMs (3B-72B) on 9 behavioral neuroscience paradigms (Morris water maze, T-maze, etc.). Qwen2.5-VL-7B achieves 52.6% success on ASCII vs 32.1% random and 78.9% rodent baselines. Scaling >7B yields diminishing returns; longer context and chain-of-thought degrade performance.
MolClaw is an autonomous agent with a three-tier hierarchical architecture (70 skills) for drug molecule evaluation, screening, and optimization. It integrates 30+ specialized resources and achieves state-of-the-art performance on MolBench, a benchmark spanning 8 to 50+ sequential tool calls. Gains concentrate on structured workflow orchestration rather than ad hoc scripting.
EnactToM is an evolving benchmark with 300 multi-agent embodied tasks in 3D household environments with partial observability. It tests functional Theory of Mind—acting optimally on implicit beliefs—rather than literal belief questions. All seven frontier models score 0.0% on hard task completion, with 93% of failures traced to epistemic coordination breakdowns.
CarbonScaling is a hardware-aware analytical framework modeling carbon footprint of frontier LLM training. It integrates neural scaling laws, distributed training strategies, accelerator modeling, and operational/embodied carbon accounting. Source code released on GitHub.
ClawForge is a benchmark framework for CLI agents testing persistent state and conflict handling. 17 scenarios, 6 ability categories. Seven frontier models evaluated: best score 45.3%, widest gap 17-90% driven by whether agents inspect existing state before acting.
Red-Bandit is a red-teaming framework that adapts real-time specialized LoRA experts for different attack styles (manipulation, slang) via reinforcement learning. A multi-armed bandit algorithm dynamically selects the optimal expert based on target model response safety. State-of-the-art results on AdvBench with more readable prompts.
Property-guided program synthesis approach reduces LLM costs by replacing simple numeric scores with formal property verification. When a property is violated, the system provides concrete counterexamples to guide repair. On PDDL planning domains, this method generates 7× fewer programs and drastically reduces evaluation costs while improving solution quality.
AASIST3 enhances speech deepfake detection by integrating Kolmogorov-Arnold Networks (KAN) into the AASIST framework. The model achieves minDCF=0.5357 (closed) and 0.1414 (open) on ASVspoof 2024, doubling prior performance. Code released on HuggingFace.
LightTransfer converts language models (LLaMA, Mistral, QwQ-STILL) into hybrid architectures without training. The method identifies lazy layers and replaces full attention with streaming attention, reducing KV cache costs. Results: up to 2.17× throughput improvement with <1.5% loss on LongBench and 53.3% on AIME24.