CODA is a GPU kernel abstraction that rewrites Transformer blocks as GEMM-epilogue programs. It fuses memory-bound operations (normalization, activations, residuals) with GEMM output before writing to memory, reducing data movement. Covers nearly all non-attention computation in forward/backward pass.
ztok is a multithreaded tokenizer library in Zig, 2–5× faster than tiktoken/HF/SentencePiece. Loads tiktoken, HF tokenizer.json, SentencePiece, TokenMonster, Mistral Tekken formats. Bit-identical to reference implementations, 8 language bindings, optimized for RAG and dataset tokenization.
SpecHop accelerates multi-hop retrieval agents by maintaining multiple speculative threads with faster but less reliable tools, asynchronously verifying predictions and committing/rolling back branches. The framework reduces latency by up to 40% while preserving accuracy and final trajectory, approaching oracle latency gains with sufficient active threads.
PALS is an LLM inference optimization system integrated into vLLM that treats GPU power caps as a controllable parameter. By combining offline power-performance models with feedback-driven control, it improves energy efficiency by up to 26.3% and reduces QoS violations by 4x to 7x across dense and mixture-of-experts models.
AutoRPA distills decision logic from LLM agents (ReAct paradigm) into robust RPA functions using a translator-builder pipeline and retrieval-augmented generation. On repetitive GUI tasks, generated functions reduce token usage by 82–96% while maintaining performance.
AgentAtlas proposes multidimensional evaluation of LLM agents beyond simple success rates. The study introduces a 6-state control taxonomy, a 9-category error taxonomy, and audits 15 existing benchmarks. On 8 models (4 closed, 4 open-weight), removing explicit labels drops accuracy by 14-40 pp, revealing strong prompt dependency.
COSMO-Agent, a tool-augmented RL framework, trains LLMs to orchestrate iterative CAD-CAE processes. The system learns to generate parametric geometry, solve simulations, and revise designs under multiple constraints. Industry-aligned dataset covering 25 component categories. Trained small LLMs outperform large open-source and closed-source models in feasibility and stability.
ConTact explicitly decomposes antibody CDR design into three stages: surface complementarity fingerprints, CDR-antigen contact prediction, and contact-gated feature injection. On CHIMERA-Bench, the model achieves 7% RMSD improvement, 10% F1 gain in epitope awareness, and 0.38 AAR sequence recovery over baselines.
JECS (Joint Envelope Conformal Selection) is a method to decontaminate LLM evaluation benchmarks by controlling global contamination rate (GCR) across multiple models. It aggregates per-model conformal p-values and applies adaptive Benjamini-Hochberg procedure to select a benchmark with provable fairness guarantees and higher power than baseline approaches.
GOEN (Geometry-Optimised Epistemic Network) combines multi-scale features, L2 normalisation, Mahalanobis distance, and calibration to detect out-of-distribution inputs. Key finding: CenterLoss degrades OOD detection (AUROC 0.9366 vs 0.9483 without), despite improving classification accuracy. GOEN-NoCenterLoss achieves 0.9483 AUROC on CIFAR-10, outperforming deep ensembles (0.8827), KNN (0.8967), and ODIN (0.8870).
TransitLM: 13M+ transit route records from 4 Chinese cities (120k stations, 13.6k lines) for training LLMs to generate routes without map infrastructure. Models learn to ground GPS coordinates to stations and produce structurally valid routes without explicit mapping.
Real-world deployment of federated learning pipeline for iron deficiency prediction from full blood count data. Uses DeepCBC (frozen haematology foundation model) + FedMAP (personalised aggregation). Tested across two clinical sites (AUMC, NHSBT) with non-IID data. FedMAP improves ROC-AUC from 0.947→0.959 (AUMC) and 0.856→0.867 (NHSBT) versus local-only training.
ACC converts agent trajectories (search, software engineering, database querying) into long-context QA pairs for SFT training. Removes tool response masking and creates explicit supervision over distant dependencies. Qwen3-30B-A3B achieves +18.1 on MRCR and +7.6 on GraphWalks, comparable to Qwen3-235B.
OGCaReBench is a retrieval-focused benchmark evaluating LLMs on off-guideline clinical questions extracted from published medical case reports. GPT-5.2 achieves 56% without retrieval, 82% with retrieved medical articles. Specialized models reach only 42%.
Token-weighted DPO (TwDPO) and AttentionPO introduce preference optimization that weights tokens by importance. AttentionPO uses the model's own attention to estimate weights without a separate reward model. Results: improvements on AlpacaEval, MT-Bench, ArenaHard.
Sem-Detect detects AI-generated peer reviews by analyzing textual features and semantic analysis at claim level. The method compares a target review against multiple AI-generated reviews of the same paper, exploiting AI model convergence versus human reviewer diversity. On 20,000+ ICLR/NeurIPS reviews, Sem-Detect improves strongest baseline by 25.5% in TPR@0.1% FPR.
RankJudge is a benchmark generator for evaluating LLMs-as-judges on multi-turn conversations grounded in reference documents. The system creates conversation pairs with a single flaw injected into one turn, enabling unambiguous labeling. Evaluation of 21 frontier LLM judges ranked via Bradley-Terry model across machine learning, biomedicine, and finance domains.
ArabDiscrim is a corpus of 293K Arabic Facebook posts (2014-2024) on racism and discrimination. It includes 200 curated terms with morphological families (13+ inflections), 20 discrimination axes, and native engagement signals (reactions, shares, comments). Released under restricted research-use license for ethical compliance.
New evaluation approach for frontier AI: 'open-world evaluations' complement benchmarks by testing complex real-world tasks over long horizons. CRUX project demonstrates an AI agent developing and publishing an iOS app to Apple App Store with only one avoidable manual intervention, revealing emerging capabilities.
PlanningBench is a framework for generating scalable and verifiable planning data. It abstracts 30+ task types and difficulty factors from real scenarios, then synthesizes problems with adaptive control and automatic verification. RL training on verified data improves performance on unseen benchmarks.
ScenePilot generates critical scenarios for autonomous driving testing via multi-objective reinforcement learning. The framework combines RSS-derived physical feasibility with an AV-risk predictor to target boundary-band scenarios: physically solvable yet causing failures. Results: +6.2 percentage points collision rate on SafeBench while preserving physical validity.
X-Token introduces cross-tokenizer knowledge distillation via two complementary loss formulations (P-KL and H-KL) using a projection matrix W. On Llama-3.2-1B, the method outperforms GOLD by +3.82 points with Qwen3-4B and +0.5 with Phi-4-Mini; two-teacher setup (Phi-4-mini + Llama-3B) gains +1.3 points.
OSCToM combines RL and surrogate models to generate observer-agent conflicts in Theory of Mind tasks. On FANToM (information-asymmetric benchmark), OSCToM-8B reaches 76% accuracy vs 0.2% for ExploreToM. Data synthesis is 6x more efficient.
DeepWeb-Bench is a deep research benchmark evaluating 9 frontier models on tasks requiring massive evidence collection, cross-source reconciliation, and long-horizon multi-step derivation. Errors stem primarily from derivation and calibration (>70%), not retrieval (12-14%). Strong and weak models fail differently: incomplete derivation vs hallucinated precision.
arXiv paper on data scaling laws: progressive coverage of a latent predictive contribution spectrum (via suffix-automaton representation) strongly correlates with empirical scaling exponent. Across 12 real corpora, log K(N) shows near-linear relationship with log N (R²≈0.96), suggesting training advances an effective frontier through a predictive state spectrum.
P2D, an LLM alignment framework, couples data selection with parameter-efficient fine-tuning by identifying task-critical attention heads. It mines high-affinity data and prunes 90% of parameters using these heads as a functional filter. Result: +8.3pp performance gain and 7.0x end-to-end speedup using only 10% of data and 10% of heads.
Authors show teacher-token reliability in reasoning self-distillation depends on position within trajectory, not local entropy. They propose Position-Weighted OPSD (PW-OPSD), applying increasing position weights to token supervision. On Qwen3-4B, AIME 2024/2025 improve by +1.0/+1.1 points; validation on DeepSeek-R1-Distill-Llama-8B and Olmo-3-7B-Think confirms gains.
EntmaxKV introduces a sparse decoding framework for entmax attention, exploiting exact zeros produced by entmax versus softmax's dense tails. Combines query-aware page scoring, support-aware candidate selection, and sparse entmax attention. Achieves 3.36× speedup (softmax) and 5.43× (entmax) on 1M context using reduced KV cache fraction.
Study across 6,620 runs showing Claude Haiku compresses 10 English intensity modifiers into 5 distinct outputs. System state context dominates lexical effect (explained variance: 0.782 vs 0.079). Near operational boundaries, model exhibits three modes: small adjustments for weak words, abstention for strong words, ceiling-pushing for 'drastically'.
Anthropic leases Colossus 1, xAI's supercomputer, for $1.25B/month through May 2029 ($40B+ total). The contract caps Anthropic's access at 11% of cluster capacity, restricting the company to a fraction of available resources.
WebVoyager benchmark on 720 browser agent tasks: MiniMax M2.5 costs 2.3× less per successful task than Gemini 2.5 Flash. GLM-5 achieves 57.1% accuracy, Kimi K2.5 shows 0% parse retry rate. Open-weight models outperform Gemini not through intelligence but reliability. True cost exceeds per-token pricing once retries compound.
Tencent releases Hy-MT2, a multilingual translation model family in three sizes (1.8B, 7B, 30B-MoE) supporting 33 languages. The 1.8B model compressed to 440 MB via 1.25-bit quantization outperforms commercial APIs from Microsoft and Doubao. The 7B and 30B variants exceed DeepSeek-V4-Pro and Kimi K2.6 performance. Includes IFMTBench benchmark and WMT26 partnership.
Masked diffusion language models (MDLMs) outperform autoregressive LLMs as world models for agentic RL. Fine-tuned SDAR-8B and WeDLM-8B achieve 4x gains on BLEU-1/ROUGE-L/MAUVE. GRPO training yields +15% absolute task-success on ScienceWorld, ALFWorld, AppWorld with Qwen3, Mistral, LFM2.5 in zero-shot transfer.
MTR-Suite is a framework for auditing and synthesizing conversational retrieval benchmarks. It includes MTR-Eval (LLM-based auditor), MTR-Pipeline (multi-agent system generating high-fidelity dialogues at 1/400th human cost), and MTR-Bench (general-domain benchmark). Code and data publicly available.
TabPFN-MT extends Prior-Data Fitted Networks to multitask in-context learning for tabular data. Trained on multi-target synthetics, the model captures inter-task dependencies and reduces inference from O(T) to O(1) forward passes. On 344 datasets (<1000 samples), it achieves rank 4.89 in multitask accuracy, competitive with single-task ensembles.
Two supervised classification paradigms (cross-entropy and contrastive learning) converge to Neural Collapse, a theoretical optimum. Authors propose NTCE and NONL, two normalized losses reaching NC in <7.5% of CE iterations, with +5.5% transfer learning improvement and +8.7% under class imbalance on ImageNet-1K.
TreeText-CTS converts irregular EHR trajectories into human-readable, source-traceable evidence units derived from XGBoost tree paths. The system improves AUPRC by 6.0–9.7 absolute percentage points on PhysioNet 2012, MIMIC-III, and PhysioNet 2019 while remaining competitive with numerical time-series models.
DG-Hard, a spectral post-hoc method, recovers capabilities damaged by fine-tuning without retraining. It applies hard SVD thresholding (Donoho-Gavish) to weight matrices to isolate task-aligned signal from residual noise. Tested on 14 (model, task) settings and 9 benchmarks, it also restores safety alignment degraded by benign fine-tuning.
FusionCell predicts standard-cell performance by fusing routed layout geometry (DeiT encoder) and netlist topology (graph transformer). Trained on 19.5k 7nm cells (ASAP7), the model achieves 0.92% MAPE on delay/power metrics and accelerates characterization by orders of magnitude versus circuit simulation.
Weasel is a trajectory selection method for offline training of web agents. It optimizes a balance between importance and diversity across states, websites, and interaction patterns, with target-centered AXTree pruning. On WebArena, WorkArena, and MiniWob, it improves out-of-domain generalization with 9.7-12.5× training speedups over standard fine-tuning on Qwen2.5-7B, Gemma3-4B, and Qwen3-8B.