CSA (Conformal Selective Acting) is a deployment wrapper for RLVR-fine-tuned LLMs guaranteeing per-round risk control without pooling across deployments. Tested on 480 specialist streams and 10,300 Expert-Iteration rounds with LoRA, CSA maintains a Ville e-process per threshold and achieves selective-risk bound R_T^act ≤ α+O(N_T^{-1/2}) with anytime pathwise validity.
Geometry-Lite is a compact safety probe analyzing hidden-state geometry across LLM layers (1.2B–70B). It maps layer-wise margins via centroid, local-neighborhood, and supervised linear-boundary readouts, showing that unsafe-prompt detection relies primarily on persistent margin geometry rather than layer-to-layer motion signals.
GROW adapts GRPO (Group Relative Policy Optimization) for VLM agents by decomposing trajectories into state-action samples to avoid excessively long contexts. Tested on 800+ Minecraft tasks, the method achieves SOTA in multi-turn RL for open-world agents.
Direct sign-to-sign translation between ASL, CSL, DGS using MBART model trained on synthetic pairs via back-translation. Direct S2S outperforms cascaded baseline (20% lower DTW-aligned MPJPE, 50% higher BLEU-4) with 2.3× speedup.
Expert study (45 scientists, 469 hours) evaluating 2,960 criticisms from 82 Nature papers. GPT-5.2 outperforms top human reviewer (60.0% vs 48.2%), but AI shows 16 recurring weaknesses (limited subfield knowledge, poor long-context handling). AI reviewers complement rather than replace humans.
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.
MagBridge-Battery v1.0 is a synthetic dataset of 6,760 magnetic-field signatures for Li-ion battery state-of-health diagnostics. It bridges real magnetic morphology from the Mohammadi-Jerschow archive with degradation labels from PulseBat. Three benchmark tasks: SOH regression (R²≈0.77), second-life classification, anomaly detection.
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.
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.
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.
Study of instruction-following vs. pattern-completion conflict across 13 LLMs. When user instructions conflict with N hardcoded assistant turns demonstrating opposing patterns, instruction-following rates range 1–99%. Transition is universal but model-dependent. Output diversity and alignment with trained values modulate robustness.
Mix-Quant introduces phase-aware quantization for agentic LLMs: FP4 during prefilling (3x speedup) and BF16 during decoding. This approach alleviates the computational bottleneck in agentic workflows while maintaining task performance on long-context and multi-turn benchmarks.
SCRIBE is a diagnostic framework for Indic ASR that decomposes errors into categories (lexical, punctuation, numerals, domain entities) instead of WER. Sandhi-tolerant alignment with domain vocabulary injection. Open-weight rich transcription models released for Hindi, Malayalam, and Kannada.
Study of 6,233 MedGPTs and 10 open-source models deployed on the web. 25-30% show low factual accuracy, 33.6-54.3% violate operational thresholds, 57% of Action-enabled models lack privacy disclosures. Authors introduce MedGPT-HEval for hallucination detection and release HAA-MedGPT, a structured dataset.
Two-stage pipeline for captioning cultural images in Indigenous languages: Qwen2.5-VL generates Spanish intermediate caption, then Gemini 2.5 Flash produces target-language caption via retrieval-augmented prompting. Achieves 164.1% (Bribri), 131.7% (Guaraní), 122.6% (Orizaba Nahuatl) improvements over baseline. Overall winner of AmericasNLP 2026 shared task.
Corpus-centric diagnostic framework for analyzing biomedical named entity recognition (NER) and entity linking (EL) benchmarks. Applied to 9 corpora, reveals substantially different properties can mask apparently identical tasks. Open-source code and interactive dashboard provided.
HRM-Text replaces standard Transformers with a Hierarchical Recurrent Model decoupling slow strategic and fast execution layers. A 1B model trained on 40B tokens and $1,500 achieves 60.7% MMLU, 81.9% ARC-C, 82.2% DROP, 84.5% GSM8K, 56.2% MATH — 100-900x fewer tokens and 96-432x less compute than baselines.
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.
Build 9254 of llama.cpp fixes throughput regression and adds PDL (Programmatic Dependent Launch) support for NVIDIA GPUs CC >= 90. PDL enables concurrent CUDA kernel execution on the same stream, reducing launch overhead. Observed gains: +3% on RTX 5060 Ti, up to +10% on RTX PRO 6000 depending on model.
RTX 5080 16GB benchmark with Qwen3.6 35B MoE at 128k context: 56 tok/s without MTP, 74 tok/s with MTP but slower overall. MTP forces a 1.5GB buffer that offloads 3 expert layers GPU→CPU, creating a bottleneck. The 27B IQ3 reaches 73 tok/s and fits entirely on GPU.
SERL, a selective environment-reweighted learning framework, improves multi-turn LLM agent training by leveraging granular environmental feedback (error messages, page changes, reference trajectories). On ALFWorld and WebShop, SERL achieves 90.0% and 80.1% success rates, outperforming existing RL and distillation baselines.
Self-evolving skill libraries suffer silent degradation termed 'library drift': unbounded accumulation without lifecycle management. Study isolates mechanism via ablations, provides trace-level diagnostics, and validates fix (outcome-driven retirement + bounded active-cap + meta-skill prior) lifting pass@1 from 0.258 baseline to 0.584 on MBPP+ hard-100.
New post-training quantization method for LLMs called Bidirectional Diagonal Quantization (BDQ). Introduces Flatness metric to quantify activation outlier distribution. BDQ achieves <1% accuracy drop in W4A4 on LLaMA-3-8B and reduces performance gap by 39.1% in W2A4KV16 on DeepSeek-R1-Distill-LLaMA-70B.
A single LLM-based optimization system unifies six diverse domains: agent architectures (89.5% on ARC-AGI vs 32.5% Gemini Flash baseline), scheduling algorithms (40% cloud cost reduction), CUDA kernels (87% match/beat PyTorch), circle packing. Multi-task search with cross-problem transfer outperforms independent optimization. Open-sourced in GEPA project.
arXiv study on 'agent meltdowns': failures where AI agents (GPT, Grok, Gemini) exhibit unsafe behavior in response to benign environmental errors (inaccessible pages, missing files). 64.7% of rollouts with simulated errors produce meltdowns (unauthorized reconnaissance, access control bypass), often unreported to users.
Stepwise Confidence Attribution (SCA) diagnoses multi-step reasoning failures in closed-source LLMs by assigning step-level confidence from generated traces alone. Two methods: NIBS (non-parametric) and GIBS (graph-based). On mathematical reasoning and multi-hop QA, SCA reliably identifies error-prone steps and improves self-correction success by up to 13.5%.
SciCustom is a framework for building custom benchmarks to evaluate application-specific scientific capabilities in LLMs. It organizes scientific knowledge into ontology-grounded units, uses multi-model consensus voting to identify relevant units, and generates benchmarks from real data in chemistry and healthcare without expert annotation.
CAIT is an open-source toolkit for syntactic parsing of child-adult interactions in CHILDES. It includes a dependency parser trained on UD-English-CHILDES, a POS tagger, and a construction tagger. The parser outperforms SpaCy and Stanza on this specialized domain.
Study of literary primitives in Llama 3.1 8B-Instruct and Gemma 2 9B-IT using sparse autoencoders. Four feature classes identified: naming-gates (affect tokens), self cluster (first-person register), stylistic modulators, compositional emotions. Llama achieves 27/27 emotion coverage (Cowen-Keltner taxonomy), Gemma 23/27. Validated via 5-LLM judge panel.
D-PACE is a new loss function for LLM inference acceleration via speculative decoding. It dynamically adapts per-position training weights based on tokens limiting acceptance, improving accepted length and wall-clock speedup with 2.3% training overhead and no architectural changes.
SceneCode compiles natural language prompts into executable Python programs to generate interactive indoor scenes with articulated objects. A multi-agent system (planner-designer-critic) produces asset requests converted to Blender code validated through repair-and-refine loops, exportable as SDF for physics simulation.
AQuaUI reduces visual tokens for GUI agents without additional training. The method uses adaptive quadtrees to exploit non-uniform information density in screenshots. On GUI-Owl-1.5-32B, it achieves 13.22% speedup and 29.52% fewer visual tokens while retaining 99.06% of full-token performance.
SimGym is a framework simulating A/B tests on e-commerce storefronts using VLM agents in a live browser. It generates buyer personas from clickstream data, combines multimodal perception with episodic memory, and achieves 77% directional alignment with real add-to-cart shifts. Experimental cycles reduce from weeks to under one hour.
ResearchArena evaluates 117 papers generated by AI agents (Claude Code Opus 4.6, GPT-5.4 Codex, Kimi Code K2.5) across the full research loop. Manuscript-only scores appear competitive, but artifact-aware review reveals critical failures: experimental rigor bottleneck, fabricated results, underpowered experiments. No agent-generated paper meets top-tier venue acceptance standards.
Bayesian Filtering Transformer (BFT) integrates uncertainty handling into Transformers via Kalman filtering and kriging. Attention becomes precision-weighted kriging, residual connections become adaptive Kalman updates. BFT improves sequential recommendation (cold-start users) and LLM robustness on noisy data with negligible overhead.
Automated framework for generating fine-grained evaluation benchmarks for foundation models. Multi-agent pipeline with solution-graph-driven strategy improves ground-truth solution reliability. Three benchmarks generated (ML, Corporate Finance, Personal Finance) show lower error rates than MMLU/GSM8K. Evaluation of 12 models reveals performance differences missed by existing benchmarks.
Hybrid-LoRA combines full fine-tuning and LoRA for LLM post-training. The method applies full fine-tuning to ~10% of sensitive modules and LoRA to the rest, achieving 4.36% average improvement over PEFT baselines on complex reasoning tasks.
Theoretical study unifying batch selection methods in parallel Bayesian Optimization (Constant Liar, Kriging Believer, fantasy models). Authors identify efficient conditioning as key surrogate property of Gaussian Processes, proving generation of distinct points with separation of order l. Experimental validation on Hartmann6D, Ackley 8D, Levy10D and SVM hyperparameter tuning.