Leaderboards, eval harnesses and ablations — the contested business of deciding which model is actually better.
arXiv:2609.06670v1 Announce Type: new Abstract: The In-context learning (ICL) paradigm aids large language models (LLMs) to adapt to new tasks without need for fine-tuning. However, selecting an opti...
arXiv:2609.06761v1 Announce Type: new Abstract: While diffusion models excel at capturing complex data distributions, scientific discovery often requires steering generation toward specific, uncharac...
arXiv:2609.06102v2 Announce Type: cross Abstract: Cellular automata is a local computation paradigm where complex behavior can arise from local interactions between simple functions. This paradigm ha...
arXiv:2609.06147v1 Announce Type: cross Abstract: Ask a language model the same question about the same document twenty times, and it sometimes returns two different answers. We built Probity, a benc...
arXiv:2602.19600v2 Announce Type: replace Abstract: Many high-dimensional datasets concentrate near a low-dimensional structure embedded in the ambient space. Generative models for such data must con...
arXiv:1907.06994v2 Announce Type: replace-cross Abstract: Mixtures of experts (MoE) are conditional mixture models in which both the mixing proportions and the component densities depend on the predi...
arXiv:2609.06869v1 Announce Type: new Abstract: Three-dimensional bioheat simulation aims to predict transient temperature distributions in biological tissue and is commonly modeled using the Pennes...
arXiv:2609.06490v1 Announce Type: cross Abstract: Recursive Super-Resolution (SR) extends fixed-scale SR to extreme magnification by repeatedly feeding predictions back into the same model, analogous...
arXiv:2609.10387v1 Announce Type: new Abstract: Deformable convolution networks have recently become popular for many computer vision tasks, especially for semantic segmentation, because of their exc...
arXiv:2604.18467v3 Announce Type: replace-cross Abstract: Motivation: Peptide-protein interactions (PepPIs) are central to cellular regulation and peptide therapeutics, but experimental characterizat...
arXiv:2609.09185v1 Announce Type: new Abstract: Multi-label chest X-ray classification has attracted considerable attention in recent years, with the effective use of visual representations and clini...
arXiv:2609.09973v1 Announce Type: new Abstract: Evaluating video captioning remains a critical challenge for Visual Large Language Models (VLLMs). Existing metrics primarily rely on matching generate...
arXiv:2609.08064v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed in settings where rare but severe harmful generations can have significant consequences. Existin...
arXiv:2609.08861v1 Announce Type: new Abstract: Benchmark scores are a central currency in model releases: they inform purchasing decisions, shape public trust, and influence policy. Yet, a key assum...
arXiv:2609.08572v1 Announce Type: new Abstract: Large language model (LLM)-based multi-agent systems (MAS) achieve strong performance by employing specialized multiple agents, yet their performance d...
PAC-CF introduces a method called Probably Approximately Correct Conformal Filtering to calibrate irreversible frontier pruning in large language model (LLM)-guided search. The approach treats tree pruning as a PAC‑guaranteed decision problem, deriving a conformal margin from score deficits on held‑out Native traces and applying a direct score‑gap filtering rule during deployment. Experiments across multiple domains and controllers show that PAC‑CF improves utility at various budgets and reduces workload metrics, notably boosting equal‑domain utility by 4.38 points and cutting physical requests by 18.95% with a 23.76% token reduction on a pruning‑aware ToolTree under a 100‑request budget.
Kalman Delta Networks (KDNs) extend linear attention models by treating associative memory as a linear–Gaussian state‑space system, enabling the Kalman filter to optimally estimate both memory state and its uncertainty. Two GPU‑friendly approximations—Diagonal KDN and Isotropic KDN—use mean‑field variational inference or a single scalar uncertainty per head, respectively, to maintain tractable uncertainty recurrences during linear‑attention scans. Experiments on 750 M and 1.3 B‑parameter models show that KDN variants consistently lower perplexity and raise downstream accuracy compared to existing linear‑attention baselines.
arXiv:2609.08149v1 Announce Type: new Abstract: SWE-Bench Pro has emerged as a standard benchmark for evaluating software engineering agents on challenging repository-level tasks. However, our analys...
The paper introduces ARC, a lightweight hierarchical policy that learns to configure LLM‑based agent systems on a per‑query basis by treating each configuration as a temporally extended option in a semi‑Markov decision process. Unlike fixed templates or hand‑tuned heuristics, ARC dynamically selects workflows, tools, token budgets, and prompts tailored to the difficulty of each query. Experiments on reasoning, tool‑use, and agentic benchmarks show that ARC outperforms budget‑matched tool‑augmented LLMs, boosting reasoning accuracy by 31.3%, tool‑use accuracy by 13.95%, and doubling success on the τ‑Bench Airline Pass task from 9.0% to 18.0%.
UnitBoost proposes a non‑generative merge operator to manage compound LLM systems, replacing the opaque higher‑level LLM that traditionally coordinates worker outputs. The operator maps worker outputs to slot‑value proposals, uses a constrained argmax to assemble the final answer, and explicitly tracks unfilled slots as residuals for subsequent rounds, thereby achieving order‑free processing and unit provenance. Across three held‑out benchmarks, UnitBoost outperforms both gold‑label‑selected candidates and input‑matched generative managers, improving compound‑system performance by up to 0.182 points and raising FanOutQA cell F1 from 0.4778 to 0.5524.