LLMs simulating users in intervention experiments produce biased observational studies. Trained on observational data, they induce implicit population drift across treatment conditions, distorting effect estimates. Authors propose negative control outcomes to diagnose bias and persona specification adjustments to mitigate drift.
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.
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.
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.
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.
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.
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.
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.
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.
LLMEval-Logic is a Chinese logical reasoning benchmark with 246 base items and 190 hard items, verified by Z3 and expert-audited. Evaluation of 14 frontier LLMs: best score 37.5% on hard items, 60.16% on Z3+rubric formalization.
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.
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.
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.
Formal Skill is a runtime abstraction for LLM agents that structures reusable capabilities via JSON metadata, action schemas, Python executors, and hook-governed control logic. Implemented in FairyClaw (open-source event-driven runtime), it replaces natural-language procedures with executable state machines, reducing token usage while improving reliability on Harness-Bench.
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.
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.
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.
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.
POLAR-Bench is a diagnostic benchmark assessing privacy-utility trade-offs in LLM agents. A trusted model with privacy policy interacts with an adversarial third-party model across 10 domains and 7,852 samples. Frontier models withhold 99% of protected attributes, but open-weight models in the 1–30B range commonly used for on-device private inference leak up to 50% of sensitive data.
Analysis of 34 frontier models (2024-2026) showing reasoning and coding capabilities cooperate (r=+0.72) but vary by lab. DeepSeek shifted from reasoning-rich to coding-first (+11.2→-4.7); Google maintains balance; Anthropic oscillates. SWE-bench saturating while HLE and instruction-following remain discriminative. Seven falsifiable predictions for next 12 months with interactive dashboard.
SAECache introduces a semantic-aware eviction policy for LLM prefix caches. Not all tokens are equally worth caching: different token types (system prompts, user queries, tool outputs, reasoning) show up to 756x variation in reuse rates. SAECache uses a multi-queue architecture with online learning to adapt priorities, achieving 1.4x-2.7x TTFT improvement over production baselines.
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.
GoLongRL presents a fully open-source post-training recipe for long-context reinforcement learning with verifiable rewards (RLVR). The authors release a 23K-sample dataset spanning 9 task types and introduce TMN-Reweight to optimize heterogeneous rewards. Qwen3-30B-A3B achieves performance comparable to DeepSeek-R1 and Qwen3-235B.
REFLECT is a meta-evaluation benchmark testing LLM judge reliability for supervising deep research agents. Authors define a fine-grained failure taxonomy (process and outcome levels) via controlled interventions on agent execution traces. Finding: best LLM judges achieve <55% accuracy on evidence verification and reasoning failure detection.
DECOR is a multi-agent framework for auditing deception in LLMs by decomposing contexts into atomic informational units and scoring four manipulation dimensions (omission, focus-shifting, meaning-obscuring). Tested on 15 frontier models, it achieves state-of-the-art deception detection on single and multi-turn benchmarks with interpretable manipulation profiles.
HalluWorld is a controlled benchmark for evaluating LLM hallucinations through explicit reference worlds (gridworlds, chess, terminal tasks). Frontier models solve perceptual hallucinations on direct observations well, but struggle with multi-step state tracking and causal forward simulation, even with extended thinking.
m3BERT is a multilingual embedding model using a Matryoshka strategy to jointly optimize representations across transformer layers and multiple embedding dimensions. Three-stage pretraining (monolingual, multilingual, web domain) outperforms existing models on Bing-Click and adapts to varying resource constraints.
HELLoRA attaches LoRA modules only to the most frequently activated experts per layer in Mixture-of-Experts models, reducing trainable parameters by 84% on OlMoE and improving accuracy by 9.2%. Tested on OlMoE-1B-7B, Mixtral-8x7B, and DeepSeekMoE across mathematical reasoning, code generation, and safety alignment.
ReCrit is a reinforcement learning framework improving LLM handling of user criticism in scientific reasoning. It decomposes behaviors into four quadrants (Correction, Sycophancy, Robustness, Boundary) using transition-aware rewards. On ChemBench, TRQA, and EarthSE, ReCrit improves accuracy from 38.15% to 51.49% on Qwen3.5-4B.
Novel transformer architecture 'block-based double decoders' combining decoder-only training efficiency with encoder-decoder inference gains. Reduces KV-cache memory and per-token compute by at least 2/3 at inference, while maintaining full loss supervision and static sequence packing during training.
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.
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.
PASC is a conformal prediction method guaranteeing simultaneous coverage across all stages in multi-stage NLP pipelines (NER → NED → entity typing, RAG, agent chains). On CoNLL-2003, PASC achieves 96.4% end-to-end coverage vs 93.4% for Bonferroni and 86.5% for independent CP, 1.7x faster, and maintains robustness under distribution shift (WNUT-17, WikiNEuRal).
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.
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.
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%.
Systematic error analysis of trajectory-based data attribution methods. Identifies optimizer mismatch (SGD vs AdamW) as dominant config-level error. Proposes AdamW-influence with 10-300% improvements in Spearman correlation across MLP, CNN, GPT-2, Llama 3.2-1B. Provides practical guidelines for data selection via K-step look-ahead framework.
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.
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.