OpenAI trains a model using process supervision (rewarding each correct reasoning step) instead of outcome supervision (rewarding final answers). This approach achieves state-of-the-art mathematical problem solving and improves alignment by directly training models to produce human-endorsed chain-of-thought reasoning.
OpenAI introduces Random Network Distillation (RND), a prediction-based reinforcement learning method that encourages exploration through curiosity. RND exceeds average human performance on Montezuma's Revenge for the first time.
OpenAI trains an agent to achieve 74,500 on Montezuma's Revenge from a single human demonstration, surpassing all published results. The algorithm replays sequences from key states in the demo and optimizes score using PPO.
Study across 6,000 task-condition pairs shows multi-agent debate degrades generation (-1.6 to -15.5pp) via critique-induced confusion, yet improves error detection (+27.4pp F1). Adversarial separation with code-execution grounding and evidence-gated generation achieves +5.3pp on generative tasks.
AURA-Mem introduces a constant-size recurrent memory (4,224 bytes) for robot policies, with a learned gate that writes only when observations would change the next action. On LIBERO-Long with OpenVLA-OFT 7B, it matches baseline policy (0.233 success) while reducing memory writes by 7× and VRAM by 6,061× versus KV-cache.
MemTrain introduces a self-supervised training framework to enhance context-memory capabilities of LLM agents. Two coupled proxy tasks on Wikipedia (masked entity reconstruction and intermediate memory recall) are jointly optimized using GRPO. Achieves gains up to 17.67 points on long-text QA and search-based QA benchmarks.
Study of representational geometry to understand how prompts reshape behavior in LLMs and VLMs. Nested decomposition framework testing translation, rigid transformation, scaling, affine and nonlinear maps on 3 LLMs, 3 VLMs and 6 datasets. Finding: cross-dimensional linear mixing (affine transformation) is the key mechanism for representational reorganization toward task structure.
GATD (Geometry-Aware Tabular Diffusion) improves tabular synthesis by incorporating pairwise angles and lengths between columns as inputs and auxiliary targets. The MLP instantiation achieves SOTA with 3.5x fewer parameters on average, winning 8/10 on Shape, 7/10 on Trend, and 9/10 on downstream utility, reducing Shape and Trend errors by 27% and 20%.
Neural network pruning method using Marchenko–Pastur random-matrix theory with minimal post-pruning fine-tuning. On ImageNet-1k, ViT-B/16 achieves 83.41% top-1 with 59.81% MAC reduction after 3 distillation epochs; ResNet50 8:16 reaches 75.87% with 1.62× A40 speedup.
ALAR (Adaptive Latent Agentic Reasoning) is a dual-mode framework alternating between compact latent reasoning and explicit chain-of-thought based on decision difficulty. Trained using agent actions as supervision, it reduces generated tokens by 43.6% in search and 84.6% in tool-use while maintaining accuracy.
ToolGate is a lightweight controller that decides whether to execute or skip tool calls proposed by vision-language agents. Across five benchmarks with Qwen3-VL, it reduces token cost to 64-69% of the ReAct baseline while preserving accuracy, and further improves accuracy by 1.65 points with matched-domain training.
arXiv paper proposing CLEAR, an optimal budget allocation method for LLM inference grounded in economic theory. Using a shifted-surge utility function and global shadow pricing, CLEAR performs rational abandonment and reallocates resources from insolvent to solvable queries. Results: 3x improvement in global accuracy vs uniform allocation under resource scarcity.
SkillDAG models inter-skill relationships as a typed directed graph for dynamic LLM agent skill selection at inference time. On ALFWorld and SkillsBench with MiniMax-M2.7, it achieves 67.1% success and 27.3% reward, exceeding Graph-of-Skills baselines by +12.8 and +8.6 points. The graph self-evolves during execution via a propose-then-commit protocol, accumulating structure across episodes.
Theoretical study of neural network loss landscape geometry. Authors prove a Spectral Alignment Decomposition explaining why curvature exponent α varies across layer types (α≈2 convolutions, α≈1 transformer attention, α<1 MLP). Empirical validation on 93 layers, 5 architectures, 3 datasets with ~2% median error.
Factorial study of 4 open-source LLMs rating clinical decisions in type 2 diabetes pharmacotherapy. LLMs as AI raters score 74–78 points under rubric-free protocol vs 7.69–49.64 points under anchored Gold Rubric. Rubric amplifies discrimination between CDSS models (1.76–5.10×) and reveals behavioral variation suppressed without rubric.
RESCAST-100K is a benchmark of 100,000 U.S. homes simulated via EnergyPlus/ResStock for evaluating cross-domain generalization in residential energy load and indoor temperature forecasting. 15-minute time series dataset with 40+ static building covariates, integrating 5 real-world datasets. Cross-attention and MLP-mixer models outperform classical transformers under domain shift.
Method to extract reasoning primitives from ReAct agent traces. Recurrent reasoning moves are clustered and converted into typed pseudo-tools. Induced libraries outperform the source agent: +44pp on RuleArena NBA, +30pp on MuSR, +22pp on NatPlan.
RRISE compresses randomized smoothing certification into a single forward pass via a learned surrogate, replacing up to 10⁴ Monte Carlo evaluations per query. Conformal calibration ensures conservative certified radii. On CIFAR-100 and Tiny ImageNet, 1.23–1.91× higher certified accuracy than prior offline-surrogate methods.
GRZO is a zeroth-order optimizer for memory-efficient LLM fine-tuning. It draws one perturbation per mini-batch example and aggregates losses via group-relative normalization, increasing effective gradient directions from one to batch size at no additional forward cost. On Llama3-8B, GRZO achieves +3.0 accuracy over MeZO with 23% lower peak GPU memory.
IdiomX is a large-scale multilingual benchmark for idiom understanding, containing 190K+ contextualized examples across 12K+ idioms in English, Arabic, and French. The dataset includes idiomatic/literal usage labels and linguistic metadata. Four tasks evaluate idiom detection, retrieval, and interpretation.
Study of 'handoff debt': the rediscovery cost when a coding agent resumes an interrupted task. Across 75 tasks and 724 runs, structured notes reduce median agent events by 20–59% and tokens by 42–63% vs. repository-only takeover. Agent benchmarks should evaluate resumption efficiency, not just task resolution.
Method to predict best-of-N inference scaling gains without running the full procedure. Ridge predictor identifies 3 stable features (prompt-level agreement spread, label-assisted first-correct-sample position, completion-length variance) plus entropy, reaching Spearman ρ=0.90 correlation with actual gains across model families and math/reasoning tasks.
EURO-5K is a 5K-sentence corpus for extracting reporting obligations from EU legislation (136 legislative acts). Comparison of fine-tuned BERT and LLMs (QLoRA): generic and legal BERT achieve similar 0.89 F1; legal pretraining helps mainly for parameter-efficient tuning. Convergence at 3K samples.
TypewriterLM is a 7.24B parameter language model trained exclusively on English text predating 1913. Authors construct TypewriterCorpus (54B tokens), a cleaned historical corpus with leakage mitigation, and introduce lexically grounded instruction tuning to ground responses in historical documents. Three datasets and History-Event benchmark are released.
SEA-Embedding is an open and reproducible text-embedding pipeline for Southeast Asian languages trained exclusively on public data. The study examines three core factors: data composition, training objective, and base encoder initialization. Achieves state-of-the-art results on SEA-BED.
Multi-agent LLM systems lose up to 72% of issue-critical facts during deliberation, creating a 'deliberative illusion'. DelibTrace measures factual attrition and stance homogenization. Agents converge toward consensus while forgetting essential elements needed to interpret the problem.
EvoTrainer co-evolves LLM policies and training harnesses via empirical feedback for autonomous agentic RL. Tested on mathematical reasoning, competitive programming code generation, and software engineering, the system matches or exceeds human-engineered RL baselines, with largest gains on long-horizon agentic SWE tasks.
Regret Pre-training introduces a self-supervised framework based on LUPI using dual-view architecture generating Student (causal) and Teacher (future-conditioned) distributions. On OLMoE-1B-7B after 4B tokens, GlobalRegret and LocalRegret achieve 33.9% and 32.2% average accuracy vs 30.2% baseline, with 18.1pp gain on BoolQ. No additional parameters.
LEDE, an offline reinforcement learning framework, optimizes LLM inference by dynamically selecting exit layer and speculation length based on local sequence context. On Llama-2 and Llama-3, it achieves 2.0×–2.7× speedup over autoregressive decoding, +17% over static speculative baselines.
Reward guidance algorithms steer generative processes toward reward-tilted measures. The paper shows reward hacking stems from finite-particle plug-in estimation of the Doob h-function in practical implementations. Authors propose a closed-form reward damping schedule and validate on Gaussian targets, 2D checkerboard, and FLUX.1 text-to-image generation.
ReLoRA is an efficient re-adaptation framework for continuously evolving LLM services. It uses Bayesian optimization to initialize LoRA adapters compatible with base-model updates, then fine-tunes with scheduled regularization. Results: up to 8.9× reduction in time-to-readiness and up to 4.6% accuracy improvement.
Fast-dLLM++ improves diffusion LLM inference by replacing homogeneous confidence token selection with Fréchet profile decoding. Training-free, it exploits heterogeneous confidence profiles to parallelize more tokens safely, achieving up to 37% higher throughput on GSM8K, MATH, HumanEval, and MBPP with LLaDA-8B while maintaining accuracy.
Microsoft announces MAI-Thinking-1 (35B, reasoning) and MAI-Code-1-Flash (5B, code). The former outperforms Claude Sonnet 4.6 in blind human evaluation. Both trained on commercially licensed data without third-party distillation.
Comparative study of learning rules (backprop, feedback alignment, predictive coding, STDP) via RSA alignment with human V1 fMRI. Backprop destroys 90% of V1 alignment after 1 epoch (r: 0.102→0.011), while PC and STDP lose only 25-31%. At epoch 40: PC/STDP >> BP/FA. Suggests fundamental trade-off between global error signals (higher layers) and early-layer alignment.
CVE-Bench evaluates 5 frontier models on 20 real-world CVEs (Pillow, GitPython, urllib3, etc.) across 300 runs. Max solve rate 50% (60% under advisory). Agents patch syntactically but leave vulnerabilities open. Significant cross-family gaps (OpenAI vs Laguna, p<0.05), within-family noise. Failure modes: wrong-search drift, hallucinations, context loss.
RealityTest evaluates whether AI systems disclose their identity when asked. Multimodal, multilingual benchmark based on 3,152 identity-probing queries from ~750 participants across 49 countries, 5 languages (text and speech). Findings: only 31% ask directly; a single suppression instruction reduces disclosure below 30% even in best-performing models.
Study of automatic assessment for massive multilingual bitext: decomposed into parallelism evaluation via multilingual embeddings and reference-free quality estimation. Benchmarks 4 embedding models and 9 evaluators on FLORES-200 covering 6,654 language-pair directions. Key finding: no single model is universally reliable; direction-aware routing and calibration required.
BudgetDraft trains a sparse drafter for speculative decoding in long-context inference (4K-16K tokens). The method exposes the model to multiple KV budgets during training and aligns each sparse view with a shared full-cache teacher target. Results: 6.55x, 4.46x, 2.10x speedup vs autoregressive decoding at 4K, 8K, 16K tokens.
TOPD (Trajectory-aware On-Policy Distillation) improves LLM reasoning by using near-future trajectory information to identify truly divergent states. On AIME24/25, TOPD reaches 63.3%/53.3% vs 60.0%/46.7% in standard OPD, showing 30% of high-loss tokens are false positives.
ProtStructQA is an executable benchmark for protein structural question answering. 382.2K questions generated from hidden domain-specific language, evaluated on Qwen3 (0.6B–8B) and Gemma-3. Key finding: capability threshold between Qwen3-1.7B and 4B where models transition from failing to produce executable denotations to mastering chain-of-thought reasoning.