Leaderboards, eval harnesses and ablations — the contested business of deciding which model is actually better.
arXiv:2609.26035v1 Announce Type: new Abstract: Conversational agents often express answers in a uniformly confident register. We test whether expressed uncertainty, provenance-aware assertion, and e...
arXiv:2609.26086v1 Announce Type: new Abstract: An agentic retrieval system issues a sequence of search queries and must decide, at each step, whether the evidence collected so far is enough to stop....
arXiv:2609.26399v1 Announce Type: new Abstract: Scene safety understanding plays a life-or-death role in situational awareness in various critical domains. Traditional methods that rely on learning d...
arXiv:2609.26468v1 Announce Type: new Abstract: A key factor in deciding whether to trust an automatic prediction is its confidence score, which should be calibrated to match the actual probability o...
arXiv:2609.26488v1 Announce Type: new Abstract: While Chain-of-Thought (CoT) reasoning has improved the capability of language models, directly applying it to Spoken Language Models (SLMs) may introd...
arXiv:2609.25007v1 Announce Type: cross Abstract: The performance of state-of-the-art speaker verification (SV) systems severely degrades on short utterances due to insufficient speaker-specific info...
arXiv:2503.15242v3 Announce Type: replace Abstract: We introduce BigO(Bench), a novel coding benchmark designed to evaluate the capabilities of generative language models in understanding and generat...
arXiv:2510.17881v4 Announce Type: replace Abstract: Large language models (LLMs) are typically aligned with population-level preferences, despite substantial variation across individual users. We int...
arXiv:2607.14109v2 Announce Type: replace Abstract: Probing the capabilities of Large Language Models (LLMs) and building robust solutions for Multiple-Choice Question Answering (MCQA) remain central...
arXiv:2609.25716v1 Announce Type: new Abstract: Reference-based image quality assessment (IQA) metrics aim to reflect how humans perceive the perceptual distance between a pair of images. To learn ho...
arXiv:2609.25891v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) often struggle with fine-grained visual perception when processing complete images, as critical evidence may o...
arXiv:2609.26056v1 Announce Type: new Abstract: Vision-Language Models (VLMs) offer promising capabilities for automated sports coaching but face a fundamental limitation: they implicitly compare aga...
arXiv:2609.26549v1 Announce Type: new Abstract: Individual tree crown segmentation from aerial imagery underpins tree-level carbon accounting, biodiversity, and restoration monitoring at landscape sc...
arXiv:2609.26151v1 Announce Type: cross Abstract: Image restoration is inherently challenging due to the diverse and highly input-dependent nature of real-world degradations. While recent architectur...
arXiv:2603.03806v2 Announce Type: replace Abstract: The state space model Mamba has recently emerged as a promising paradigm in computer vision, attracting considerable attention for its efficient ha...
arXiv:2609.26142v1 Announce Type: cross Abstract: Augmented inverse-probability weighting (AIPW), targeted maximum likelihood estimation (TMLE), and double/debiased machine learning (DML) are three r...
The paper introduces an adequacy‑aware calibration protocol for generative social simulators that integrates amortized posterior estimation, synthetic identifiability assessment, matched‑sample‑size adequacy checks, diagnosis‑guided repair, and held‑out audits. Applied to a second‑hand luxury resale market, the protocol reveals that behavioural parameters are recoverable but calibration is approximate and overconfident for one parameter, and that the simulator’s reachability reference is violated in every cell, particularly in mean purchased tier. The repair improves two of four cells but fails to restore full adequacy, and a held‑out audit uncovers a buyer‑breadth‑dispersion miss not detected earlier; profile‑source ablation shows language‑model‑derived persona profiles outperform a flat rule baseline, though within‑category brand relabelling has no consistent effect. whyItMatters":"The study demonstrates that without an adequacy check, generative social models may appear valid descriptively yet fail to capture key emergent network structures, highlighting the need for rigorous calibration protocols in social simulation research."
The paper investigates how reinforcement learning can unintentionally obscure the chain‑of‑thought (CoT) reasoning in vision‑language models, making their internal reasoning less traceable. By analyzing activation patterns, the authors show that template‑associated activations become less distinguishable during RL and that targeted interventions can mitigate this effect. They introduce TAME, a method that uses sparse autoencoders to suppress these problematic activations while still encouraging accurate behavior, achieving significant gains in CoT monitorability across multiple datasets and model families.
The paper investigates whether the performance of anomaly detection systems can be predicted without labeled anomalies. For kNN-based detectors, it derives a lower bound on AUC that links detection performance to the separation and variance of inlier and outlier scores, and uses this to analyze how density variation, intrinsic dimensionality, and domain mismatch affect score variability. The authors introduce pseudo‑anomaly probes that provide a reference for estimating relative score separation, and demonstrate through experiments on DCASE benchmarks that these probes enable anomaly‑free model selection to outperform conventional development‑set selection, especially under domain shift.
The paper introduces a benchmark of 3,600 exact‑rational word problems and 8,600 prompts that test whether language models give the same canonical answer when the same quantity is expressed in different numeric forms (decimal, fraction, percentage, number word, scientific notation, or unit‑converted). After normalizing answer syntax, canonical accuracy is high (0.969–0.996), but correctness across equivalent representations drops to 0.848–0.981, revealing that many errors stem from the evaluator’s number grammar rather than the models’ reasoning. The study also finds that representation consensus does not outperform paraphrase consensus on low‑error subsets and that certain models (e.g., Mistral Small 4) exhibit systematic unit‑conversion errors.