SONIC‑O1 is a new benchmark designed to evaluate multimodal large language models on audio‑video understanding. It contains 60 hours of 231 clips across 13 real‑world conversational domains, with 4,958 human‑verified annotations and demographic metadata. The benchmark tests open‑ended summarization, multiple‑choice question answering, and temporally grounded reasoning, revealing performance gaps between model families and across demographic groups.
By Ahmed Y. Radwan, Christos Emmanouilidis, Hina Tabassum, Deval Pandya, Shaina Raza
TensorCommitments (TCs) is a lightweight, tensor-native proof‑of‑inference scheme that enables verifiable inference for large language models (LLMs) without requiring the verifier to rerun the model or possess a powerful GPU. By binding each inference to a commitment stored in multivariate Terkle Trees, TCs detect tampering with only a 0.97% overhead for the prover and 0.12% for the verifier on LLaMA2. The approach improves robustness against tailored LLM attacks by up to 48% compared to previous methods that needed a verifier GPU.
By Oguzhan Baser, Elahe Sadeghi, Eric Wang, Nico Vergauwen, Sam Kazemian, Hong Kang, Sandeep P. Chinchali, Sriram Vishwanath
The paper introduces TRACE, a method that removes duplicated text—known as note bloat—from clinical notes by leveraging EHR metadata and frequency-based de‑duplication. Across 5.3 million notes from diverse patient cohorts, TRACE eliminated 47.3 % of chart text while preserving information extraction and prediction performance, with only 0.3–6.6 % of removed content being author‑generated. The authors project that applying TRACE could yield net savings of $1.00 M to $13.58 M over three years at a large academic center, depending on model pricing schemes.
By Jordan L. Cahoon, Chloe Stanwyck, Asad Aali, Rachel Madding, Sulaiman S. Somani, Emma Sun, Yixing Jiang, Renumathy Dhanasekaran, Emily Alsentzer
GENSCRIPT is an inference‑only pipeline that generates synthetic data without training a generative model. It creates a deterministic statistical profile of the source data, uses a language model to infer field semantics and cross‑column constraints, and then compiles these into an executable sampler that works for single‑table, temporal, and relational data. The method builds generators in minutes, samples large datasets quickly, and achieves fidelity comparable to leading methods while preserving key data relationships such as 1‑to‑1 mappings and primary‑foreign key constraints.
By Zilong Zhao, Abdul Raheem, Jiayu Li, Sohei Arisaka, Darius Lim Hong Yi, Milad Abdollahzadeh, Uzair Javaid, Biplab Sikdar
The paper introduces a formal framework for measuring AI propensities—tendencies of models to exhibit particular behaviours—using a bilogistic formulation that identifies an "ideal band" of success probability. It estimates the limits of this band with task‑agnostic rubrics and applies the method to six families of LLMs, showing how shifts in propensity affect task performance. The study finds that propensity estimates from one benchmark predict behaviour on held‑out tasks and that combining propensity with capability metrics yields stronger predictive power than either alone.
By Daniel Romero-Alvarado, Fernando Mart\'inez-Plumed, Lorenzo Pacchiardi, Hugo Save, Siddhesh Milind Pawar, Behzad Mehrbakhsh, Pablo Antonio Moreno Casares, Ben Slater, Paolo Bova, Peter Romero, Zachary R. Tidler, Jonathan Prunty, Luning Sun, Jose Hernandez-Orallo
Autoregressive transformers trained on limited trajectories of nonlinear dynamical systems can extrapolate to unseen parameter regimes, reproducing period-doubling cascades, chaotic dynamics, and attractor structures with high fidelity. In the logistic map, the model captures successive period doublings up to period 128, achieving a scaling ratio within $5 imes10^{-4}$ of the Feigenbaum constant. The study also shows how control‑parameter information is processed via attention, shaping the closed‑loop dynamics during training.
By Yilun Liu, Yi Zhang, Ganyu Wu, Sikuan Yan, Mengyue Wang, Alois Knoll, Volker Tresp, Yunpu Ma
The paper introduces RATIO, a framework for improving quantized reasoning models by identifying overthinking tokens and applying token-specific penalties. It uses Quantization-aware Reasoning Behavior Analysis to detect problematic tokens and Token-Specific Penalty Determination to assign penalties without extra training. Experiments show RATIO outperforms existing methods, boosting accuracy by up to 9.8 points and shortening chain-of-thought length by up to 51.3%.
By Chengzhu Bao, Xianglong Yan, Tianao Zhang, Jiaqi Chen, Shaoqiu Zhang, Yulun Zhang
The paper reviews one‑pixel attacks (OPAs), highlighting their extreme adversarial fragility across domains such as medical diagnosis, autonomous driving, biometrics, and quantum communication. It presents a PRISMA‑guided synthesis of studies from 2017 to 2026, offering a unified taxonomy that covers algorithmic foundations, black‑box evolutionary optimization, hybrid and program‑synthesis attacks, defence mechanisms, interpretability tools, and domain‑specific vulnerabilities. The review identifies dominant Differential Evolution strategies, emerging efficiency‑optimized and saliency‑guided methods, and gaps in dataset diversity, transferability, and evaluation standards, while proposing future research directions and a regulatory framework for robustness testing and AI security governance.
By Mirza Niaz Morshed, Md. Masudul Islam, Galib Muhammad Shahriar Himel, Md. Aslam Uddin, Hui Liu, Md. Shafiqul Islam
4DMulti is a physics‑guided learning framework that automates multicomponent identification from large‑scale four‑dimensional scanning transmission electron microscopy data. It leverages a 6‑million‑pattern diffraction database, a retrieval‑conditioned latent diffusion transformer (Sim2real) for realistic pattern generation, and a rotation‑invariant convolutional network for phase classification, achieving 98.82% accuracy on a five‑phase benchmark. The method introduces a diffraction‑inferred structural complexity metric and produces single‑nanometer‑resolution structural maps of complex material interfaces such as superconducting heterostructures, corroded alloys, and degraded solid‑state battery interfaces.
By Haoran Zhang, Zian Mao, Shufen Chu, Xiaoya He, Yuyan Guan, Antong Yang, Mingze Li, Xiaoqin Zeng, Yujun Xie
DynGhost is a transformer-based architecture designed for dynamic ghost imaging, addressing two key limitations of prior methods: the lack of temporal coherence exploitation and the assumption of Gaussian noise. It alternates spatial and temporal attention blocks and incorporates a quantum-aware training framework that uses realistic detector simulations (SNSPDs, SPADs, SiPMs) and Anscombe variance-stabilizing normalization to handle Poissonian noise. Experiments on multiple benchmarks show that DynGhost surpasses traditional reconstruction methods and existing deep learning models, especially in dynamic and photon-starved scenarios.
By Vittorio Palladino, Ahmet Enis Cetin
The paper investigates PRM‑Pruned Fragment Grafting (PPFG), an inference‑time technique that extracts high‑reward prefixes from a pruned chain‑of‑thought and grafts them into a sibling decoding process. Experiments on Qwen2.5‑7B‑Instruct with Math‑Shepherd across 500 MATH problems and multiple seeds show that PPFG performs statistically indistinguishable from a parallel‑CoT baseline, with only 14% of grafts targeting genuinely struggling chains. The study extends across three language models, six benchmarks, and multiple PRM configurations, concluding that PPFG’s inertness is not due to heuristic specifics and providing an equivalence‑testing framework for mechanism nulls.
By Khawaja Murad ul Hassan, Mehran Ebrahimi
The paper introduces FedCausalCompose, a causal world‑model framework designed for modular large‑language‑model agents that interact with distinct services such as order, payment, inventory, and shipment. It demonstrates that standard observational world models suffer from irreducible interventional errors when unblocked back‑door paths exist, whereas incorporating intervention‑response evidence improves interface recovery and can outperform non‑causal baselines when coverage and local mechanism errors are controlled. Experiments show that causal interfaces are most beneficial in structured tool environments with clear API signatures, while they provide little advantage in dialogue or narrative settings unless the causal information becomes directly relevant to the agent’s decision making.
By Xinyuan Song, Zekun Cai
The study evaluates whether detailed, profession‑specific system prompts improve performance on scientific tasks. Using an open‑source corpus of 503 agent profiles and Gemini 3.8 Flash, the authors compared matched profiles to four control prompts across nine text‑based science benchmarks and a tool‑using bioinformatics benchmark. Results show no consistent accuracy gains; matched profiles actually increased token usage and cost, and in some cases reduced success rates, with only a minor advantage in one benchmark likely due to prompt length rather than domain expertise.
By Timothy Kassis
The paper introduces Rules to Tools (R2T), a system that provides executable checks for scientific coding agents to verify compliance with public scientific requirements. In experiments across multiple task cohorts, agents using R2T’s prepared checks achieved high repair success rates—26/30 with text and 29/30 with checks—while also demonstrating varying task preferences and cost trade‑offs. The study quantifies how tool‑enabled checks influence repair outcomes and agent‑side resource usage in scientific computing contexts.
By Jingjie Ning, Guojiang Zhao, Chen Xu, Shanshan Zhong, Xiaochuan Li, Ji Zeng, Guolin Ke
Graph Hierarchical Recurrence (GHR) is a new framework that enhances Graph Neural Networks and Graph Transformers by jointly processing the input graph and a pooled hierarchical abstraction. It addresses the limitation of existing models in handling predictions that depend on correlations between distant graph regions, especially under out-of-range generalization where test instances require interactions beyond training distances. Across many long-range benchmarks, GHR consistently improves performance, achieving state‑of‑the‑art or competitive results on multiple tasks.
By Stefano Carotti, Marco Pacini, Alessio Gravina, Davide Bacciu, Bruno Lepri, Sebastiano Bontorin
Large language model agents depend on external harnesses to exchange information with their environment and to recover from execution errors, but recovery is typically evaluated only by overall task success, masking a key trade‑off. The authors treat recovery as a causal decision problem, comparing outcomes with and without recovery from the same execution state to separate rescue from harm and analyze how its value evolves over time. They propose the Causal Intervention Router (CIR), a lightweight policy that uses pre‑recovery information to decide when intervention is beneficial, achieving a 3‑point increase in success on long‑horizon ALFWorld tasks with Qwen3‑14B while preserving correct observations and demonstrating that recovery’s benefit is not solely due to new observations.
By Shuyao Xiao, Shengling Wang, Xuan Chen, Ke Chao, Ming Cui, Feifei Qian, Chaoyang Mei, Fanlin Meng, Ziming Yu, Junxi Yin
arXiv:2610. 00416v1 Announce Type: new Abstract: Evaluating large language models becomes increasingly challenging as their capabilities advance: benchmarks can saturate, public test sets risk contamination, and assessing harder tasks can require expensive grading or execution infrastructure.
By Timoth\'ee Lesort, Alejandra L\'opez de Aberasturi G\'omez, Tristan Karch, Tom Veniat, Philippe Modard, Karl Tuyls, Ludovic Denoyer
The paper "Science or Slop?: Benchmarking and Mitigating Scientific Slop in AI-Generated Papers" introduces SciSlopBench, a dataset of 390 AI‑generated papers paired with human‑written counterparts, and defines six measures across Structure, Argument, and Artifacts to detect scientific slop. The authors show that these measures can identify AI papers with 85.9% accuracy and that higher slop correlates with lower ICLR ratings and distinguishes rejected from accepted papers. They also propose SciSlopHarness, a framework that guides a fixed LLM to revise only evidence‑supported sections, reducing the AI‑human gap by 63% without human reference targets.
By Yerim Oh, Young-Jun Lee, Jaewoo Ahn, Gunhee Kim, Dongyeop Kang
Legal Research Bench (LRB) is a new benchmark comprising 413 open-ended U.S. legal research questions, each paired with a gold answer, supporting authorities, and a binary grading rubric. The study evaluates thirteen advanced language‑model agents using web search, case‑law search, page parsing, and retrieval tools, scoring responses only when all required criteria are met and cited authorities verify. Results show that even the best model, Claude Opus 4.8, achieves full correctness on only 42.9% of questions, with performance varying by legal area and task complexity, and no clear link between more tool calls or inference cost and higher accuracy.
By Katrina Drozdov, Oliver Chen, Langston Nashold, Rayan Krishnan
The paper introduces Quarantined Expert Shutdown (QES), a new backdoor containment strategy for large language models. QES allows backdoor learning to occur during training but routes it into a designated, quarantined expert that can be disabled at deployment. The method achieves significant reductions in attack success rates while largely preserving model utility.
By Jianwei Li, Min-Seon Kim, Jung-Eun Kim