Mosaic, a probabilistic weather forecasting model, addresses three spectral degradation failures in ML-based prediction: spectral damping, high-frequency aliasing, and residual leakage. With 214M parameters at 1.5° resolution, it matches models trained 6× finer and generates well-calibrated ensembles in 12s for 10-day forecasts on H100.
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.
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.
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.
FishBack proposes activation steering using pullback Fisher geometry for transformers. Authors show activation space is non-Euclidean (>97% deviation on GPT-2) and derive closed-form optimal steering equation. Method outperforms CAA, ActAdd, ITI by 1.3×–2.5× on off-target KL reduction.
DISA is an offline RL method for LLMs that decouples partition-function estimation (via importance sampling) from policy optimization. On 9 benchmarks (math and code), it matches or exceeds FlowRL, outperforms GRPO/GSPO, and retains substantially more strategy-level diversity than reward-maximization baselines.
TClone is a forkable personal workspace system for computer-use agents. It enables snapshotting, forking, and rollbacking live GUI environments with fast branching and isolation. Uses sibling containers, copy-on-write memory sharing, filesystem versioning, and asynchronous checkpointing. Reduces total task latency by 1.9x vs KVM and 1.5x vs CRIU.
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.
Systematic optimization of real-time diffusion models on Apple M3 Ultra (60-core GPU, 512 GB unified memory). CoreML conversion of SDXS-512 distilled model combined with 3-thread camera pipeline achieves 22.7 FPS at 512×512 resolution. Demonstrates that CUDA optimization insights do not transfer to Apple Silicon's unified memory architecture.
Study on how representation geometry organization in language models depends on scale. Subspace PGA metric tests alignment of intermediate geometry with unembedding matrix readout. Small models (≤1024) progressively lose organization at late layers during training, while large models (≥2048) preserve it throughout. Scale determines how geometry organizes for prediction.
Behavioral evaluation methodology for agentic AI systems: scoring intermediate decisions via LLM judge ensemble across 6 dimensions (regime detection, routing, adaptation, risk calibration, strategy coherence, error recovery). Behavioral score correlates at rho=0.72 with Sharpe ratio. Closed-loop reinforcement (SAC) reduces MAPE from 0.61% to 0.54% on 2017-2025 test set.
WebGameBench is a requirement-to-application benchmark evaluating whether coding agents can convert a web game specification into a browser-playable application. Across 111 tasks and 12 agents, the best configuration achieves 76.9% usable rate but only 20.2% excellent rate, revealing a gap between minimum delivery and full requirement satisfaction.
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 36.3pp on constraint satisfaction across seven benchmarks.
New MoLF (Mixture of LoRA and Full) method for LLM adaptation that dynamically routes gradients between full fine-tuning and LoRA at optimizer level. Tested on Gemma-3-1B, Qwen2.5-1.5B/3B across SQL, Medical QA, and counterfactual knowledge tasks. MoLF-Efficient outperforms adaptive LoRA approaches by 20% (Fact) and 9% (Med/SQL). Code open-sourced.
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.
UbuntuGuard is the first policy-based safety benchmark for African languages. Built from 155 domain experts, it evaluates 15 models (7 general-purpose LLMs, 8 guardian models) across three variants. Findings show English-centric benchmarks overestimate real-world multilingual safety and cross-lingual transfer remains insufficient.
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), embedding this inversion signature into RL reward regularization, outperforming baselines across multiple reasoning benchmarks.
LiRA, a lightweight fine-tuning framework, improves LLM adaptation to low-resource languages through cross-lingual semantic alignment. Combines Arca (anchor-based alignment to English) and LaSR (language-aware head). Theoretical stability guarantees. Multilingual dataset (7 Asian languages) and code released.
Empirical study of RL post-training for diffusion-based code generation. Authors propose execution-free rewards (static checking) and AST-hint-conditioned sampling to overcome the "capability cliff". Static checking improves DiffuCoder from 53.9 to 67.1 on HumanEval and reduces rollout time by 9.4%.
SomaliWeb v1: Somali corpus of 819,322 documents (~303M tokens) with BPE-16K tokenizer and language-identification benchmark. Reveals critical defects in HPLT v2 (17.3% exact duplicates, 56.1% mojibake, 10.7% near-duplicates). Tokenizer 40.2% more efficient than cl100k_base on FLORES-200.
DashAttention introduces a differentiable hierarchical 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 outperforms FlashAttention-3.
CAM-VFD detects video deepfakes by analyzing cross-modal contradictions (appearance, motion, depth) using cross-attention fusion. Achieves 95.31% on GenVidBench and 93.43% on GenVideo with robustness to compression and adversarial perturbations.
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.
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.
DynMuon extends Muon by replacing update M with U·Σ^p·V† using dynamic parameter p. Theory shows positive p accelerates signal contraction early by emphasizing high-curvature directions, while mildly negative p reallocates update strength to low-curvature directions late in training. DynMuon reduces steps to target loss by 10.6-26.5% versus Muon.
LoopQ is a post-training quantization (PTQ) framework for recursive language models (LoopLMs) that reuse Transformer blocks. It addresses three challenges: distribution shift across roles, state reuse between loops, and recursive error accumulation. Under W4A4 quantization, LoopQ improves downstream accuracy by 68.8% and reduces perplexity by 87.7% versus static PTQ baseline.
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.
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%.
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.
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.
ConsumerSimBench, a benchmark built from 1,553 Chinese social-media topics and 23,122 reaction criteria, evaluates whether LLMs can reconstruct real consumer reaction patterns. Gemini-3.1-Pro achieves only 47.8% coverage of criteria, revealing a major gap between technical performance and socially grounded consumer intuition.
Systematic audit of two critical vulnerabilities in clinical AI: adversarial fragility and cross-lingual drift. On CheXNet (DenseNet121), accuracy collapses from 89.3% to 62.0% under imperceptible FGM perturbation (epsilon=0.021). Llama3.1:8b and NatLAS show major degradation on Nigerian Pidgin and Yoruba (80%→65%, 85%→55%). Standard defenses fail.
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.
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.
AdaptiveLoad optimizes video diffusion Transformer training (DiT, MMDiT) by addressing load imbalance from quadratic attention complexity. Two components: dual-constraint adaptive load balancing and fused LayerNorm-Modulate CUDA kernel. On Wan 2.1: computational imbalance reduced from 39% to 18.9%, peak VRAM utilization +22.7%, training throughput +27.2%.
MINE (Mechanistically Interpretable Neural Encoding) applies mechanistic interpretability tools to neural encoding models to localize visual features driving voxel-level activity in human visual cortex. Validated via image generation and counterfactual editing: inserting/removing predicted features shifts neural activation as expected.
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.
STT-Arena is a benchmark of 227 interactive tasks measuring LLM ability to replan under spatio-temporal dynamics. Claude-4.6-Opus achieves under 40% accuracy. Authors identify three recurring failure modes and propose STT-Agent-4B combining iterative trajectory refinement with online RL.
RTPurbo transforms LLMs into sparse models in ~100 training steps. The approach exploits three observations: only certain heads require full attention, long-range retrieval uses a 16D subspace, and dynamic top-p selection outperforms fixed top-k. Results: 9.36× prefill speedup at 1M tokens, 2.01× decode speedup, accuracy preserved.