SVFSearch is a multimodal benchmark for short-video frame search in the Chinese gaming domain. It contains 5,000 test examples and 4,198 training examples based on real game scenes. Evaluation compares direct QA, RAG, Plan-Act-Replan agents, and learned search models: best open-source model reaches 66.4%, best practical agent 79.1%, oracle 95.4%.
LEAP introduces end-to-end unstructured pruning for LLMs via per-weight Bernoulli-Gumbel-sigmoid relaxation. Across five model families (0.5B–8B) at 50–60% sparsity, LEAP improves average zero-shot accuracy by +2.59 points over ADMM baseline.
SCICONVBENCH benchmarks LLMs on multi-turn clarification of ill-posed scientific problems across fluid mechanics, solid mechanics, materials science, and PDEs. Best models resolve only 52.7% of disambiguation cases in fluid mechanics, but perform better on inconsistency detection. Evaluates clarification behavior, conversational grounding, and specification fidelity.
Systematic optimization of real-time diffusion models on Apple M3 Ultra (60-core GPU, 512 GB unified memory). CoreML conversion of distilled SDXS-512 combined with 3-thread camera pipeline achieves 22.7 FPS at 512x512 resolution. Demonstrates that CUDA optimization insights don't transfer to Apple Silicon's unified memory architecture.
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.
ProxyKV introduces a cross-model proxy pruning framework to accelerate long-context LLM inference. A lightweight Small-Model Proxy asynchronously scores KV cache importance for the target model. Tested on Llama-3.1, Qwen-2.5, and Qwen-3: recovers 98.7% of KVZip accuracy with up to 3.21× prefilling speedup (Llama-3.1-8B, dual-GPU) and sustains gains up to 170k tokens.
EPIC (Efficient Preference-aligned Index Construction) optimizes on-device RAG by storing user preferences instead of raw data. Across 4 benchmarks, reduces indexing memory by 2404×, improves preference-following accuracy by 20.17 percentage points, and lowers retrieval latency by 33.33×. Memory footprint under 1 MB with 29.35 ms/query.
Charon is a unified, modular simulator for predicting large-scale LLM training and inference performance. It achieves prediction error under 5.35% overall and 3.74% for training on GPU clusters. In inference deployment, it identified a configuration improving system throughput over engineering-tuned baselines.
KVCapsule compresses KV cache in vision-language models during autoregressive decoding. The method exploits structured attention patterns in vision tokens to achieve 2x TPS improvement and 2.4x memory reduction at 60% compression ratio, without modifying the pretrained backbone.
PH-Dreamer embeds Port-Hamiltonian physical principles into world models to improve latent imagination. The framework models energy evolution, estimates the Hamiltonian from proprioceptive observations, and uses an energy-guided Actor-Critic. Results: 4.18-8.41% phase space volume reduction, up to 7.80% energy consumption decrease, up to 9.38% jerk reduction.
Study of membership inference attacks (MIA) on masked diffusion language models (MDLM). Researchers extract 46-dimensional feature vectors from reconstruction loss at different masking ratios and train XGBoost and MLP classifiers. On MIMIR benchmark, XGBoost achieves AUC 0.878 (peak 0.930), outperforming SAMA baseline by 0.062 AUC. ELBO trajectory alone drives most of the signal.
Transformer-based world model for video frame prediction. Formulates next-frame prediction as structured probabilistic inference with latent token correspondence variables. Each token either copies from previous frame or generates new. SOTA on 4 benchmarks: 72.5% return and 35.6% score on Craftax-classic (vs 67.4%/27.9% prior).
MINE (Mechanistically Interpretable Neural Encoding) applies mechanistic interpretability to neural encoding models to identify visual features driving activation in individual voxels of human visual cortex. Using language-aligned image representations and counterfactual editing, the approach causally validates fine-grained selectivity in category-selective brain regions.
GRASP is an agentic system for multi-hop question answering that decomposes queries into dependency-aware plans and uses a three-layer hierarchical graph (entities, propositions, passages). On MuSiQue, 2WikiMultihopQA, and HotpotQA, GRASP achieves highest accuracy while consuming 40-50% fewer tokens than IRCoT+HippoRAG2.
PIMSM embeds physical constraints into a multi-scale Mamba architecture to improve representation stability under distribution shift. The model aligns discretization parameters to characteristic frequencies in temporal series (fMRI, weather). Results: improved robustness on Human Connectome Project and Weather-5K with minimal MAE in out-of-distribution forecasting.
PopPy is a system that automatically uncovers parallelization opportunities in Python compound AI applications (multiple ML model calls). On real-world applications, PopPy achieves up to 6.4× speedups in end-to-end execution time, combining an ahead-of-time compiler and runtime to handle language complexity, dynamic dispatch, and variable mutation.
Tabular foundation models (TFMs) for healthcare are distilled into lightweight models using context-leakage-aware distillation. Across 19 medical datasets and 6 TFMs, student models retain ≥90% of teacher AUC while running 26× faster on CPU, preserving calibration and fairness critical for health applications.
Alignment evaluation across 75 LLMs benchmarked against 376 humans. Qualitative analysis derives 6 themes of optimal AI functioning (Performance, Adaptive Capacity, Social Good, Ethics and Responsibility, Relational Integration, Agency). Models reproduce human value ordering but systematically exaggerate differences. Profile fidelity does not correlate with model size or recency.
BPO, a three-stage framework (bootstrapping, extrapolation, refinement), creates a self-improving data flywheel to train robust reasoning models for long-horizon sparse-reward planning. Uses planning quaternions, long-short chain-of-thought fusion, and complexity-stratified curriculum learning. SOTA on ALFWorld, ScienceWorld, WebShop with significant token efficiency.
R-AIRL (Reasoning Adversarial Inverse Reinforcement Learning) infers process-level reward functions from expert Chain-of-Thoughts without explicit reward definitions. Tested on GSM8K, MMLU-Pro, and MedReason: improves pass@1 by 17.4 points via inference-time reranking, outperforms SFT in post-training, localizes reasoning failures with 86.1% accuracy.
Scales++ proposes benchmark subset selection based on intrinsic task properties rather than model-specific failure patterns. Using 0.25% of data on Open LLM Leaderboard, it predicts full scores with 3.2% mean absolute error, reducing selection cost by 18x.
Systematic study of JEPA world models for physical planning. Authors characterize technical choices (architecture, training objective, planning algorithm) and propose a model outperforming DINO-WM and V-JEPA-2-AC on navigation and manipulation tasks. Code, data and checkpoints released.
AgentArk distills multi-agent intelligence into a single LLM through three hierarchical strategies: reasoning-enhanced fine-tuning, trajectory-based augmentation, and process-aware distillation. The resulting model preserves single-agent computational efficiency while retaining multi-agent reasoning and self-correction capabilities.
RaBiT introduces residual-aware binarization training for 2-bit LLM quantization. It solves inter-path feature co-adaptation by sequentially deriving each binary path from a shared full-precision weight, ensuring error correction across layers. Results: SOTA performance, 4.49× inference speedup on RTX 4090.
LiTS is a modular Python framework for LLM reasoning via tree search. It decomposes search into three reusable components (Policy, Transition, RewardModel) compatible with MCTS and BFS. Tests on MATH500, Crosswords, and MapEval demonstrate component-algorithm orthogonality. Key finding: LLM policy diversity, not reward quality, is the bottleneck in infinite action spaces.
DataClawBench is a benchmark for exploratory real-world financial data analysis agents, containing 2.06 million raw records and 492 cross-domain tasks. Evaluation of 8 advanced LLMs shows that increased exploration does not reliably translate to task progress or correct answers.
GCPO (Group Cooperative Policy Optimization) replaces competitive rollout optimization with team-level credit assignment. Rollouts are rewarded by contribution to valid solution coverage (determinant volume over semantic embeddings), not individual accuracy. Results: improved reasoning accuracy and solution diversity across benchmarks.
LegalCheck automates drafting of objection response letters in Dutch municipalities using RAG and context-augmented generation. Deployed in Amsterdam, the system generates near-final legal drafts in minutes instead of hours, capturing 80–100% of essential content while maintaining expert review and legal consistency.
arXiv paper showing LLMs exhibit a knowing-doing gap in tool use: recognition of tool necessity vs. actual invocation diverge. Testing 4 models on arithmetic and factual QA reveals 26.5-54% mismatches. Hidden state probing shows cognition and action signals become nearly orthogonal in late layers, with most failures at the cognition-to-action transition, not in recognition itself.
OpenDeepThink uses Bradley-Terry aggregation to select best solutions from multiple parallel candidates. The system randomly compares answer pairs, aggregates votes, and preserves top candidates for mutation. On Codeforces, Gemini 3.1 Pro gains +405 Elo points across 8 LLM calls (~27 min). Authors release CF-73, 73 expert-annotated problems.
Study showing diverse ensembles of monitors detect misaligned AI agent actions better than homogeneous ensembles. 12 GPT-4.1-Mini monitors (prompting + fine-tuning) evaluated on coding tasks: best 3-monitor ensemble achieves 2.4x greater detection gain versus identical-monitor ensemble, with strong generalization on independent data.
Researchers formalize activation steering (LLM control without retraining) as budget-constrained optimization over intervention layer and coefficient. They introduce concept granularity to measure directional heterogeneity and present GRACE, a framework using activation geometry to diagnose steering difficulties and reduce required evaluations by 39.8% on average.
DexWild introduces a low-cost system for collecting dexterous manipulation data through natural human hand gestures. The framework co-trains on human and robot demonstrations, achieving 68.5% success rate in unseen environments (4× better than robot-only) and 5.8× improved cross-embodiment generalization.
Study shows unlearning in LLMs merely suppresses information: minimal fine-tuning restores original behavior. Representation-level analysis framework (PCA, CKA, Fisher information) reveals four forgetting regimes by reversibility and catastrophicity. Identifies cases of seemingly irreversible targeted forgetting.
FinTagging is a benchmark for evaluating LLMs on extracting and tagging financial data with XBRL. It decomposes the task into two stages: FinNI (financial numeric identification) and FinCL (mapping to full US GAAP taxonomy). Testing shows models generalize well in extraction but struggle significantly with fine-grained concept linking.
PROF, a data curation method, combines Process Reward Models (PRM) and outcome rewards (ORM) to improve reinforcement learning on reasoning tasks. It filters training samples by keeping correct responses with strong process support and incorrect responses with weak process support, avoiding instability from direct PRM optimization.
New metric called Refusal Index (RI) measures LLMs' ability to refuse questions beyond their knowledge. RI correlates refusal probability with error probability using Spearman's rank correlation. Testing across 16 models and 5 datasets shows LLM refusal behavior remains fragile despite high factual accuracy.
DocReward is a document reward model evaluating structure and style of professional documents, independent of textual quality. Trained on DocPair (117K document pairs, 32 domains), it outperforms GPT-4 by 14.6 percentage points and effectively guides agents via RL toward higher structural and stylistic professionalism.
Beacon is a diagnostic benchmark measuring sycophancy (bias toward user agreement) across 12 SOTA models. Authors decompose this bias into stable linguistic and affective sub-biases, proposing prompt-level and activation-level interventions to modulate them. Sycophancy emerges from a structural trade-off between truthfulness and polite submission.
PyHealth 2.0 is an open-source clinical deep learning toolkit reducing barriers to medical AI research. It unifies 15+ datasets, 20+ clinical tasks, 25+ models, and 5+ interpretability methods in a single framework supporting signals, imaging, and electronic health records. Delivers 39x speedup and 20x memory reduction, with 400+ community members.