TrajFusionNet+ is a transformer-based model that predicts pedestrian crossing intention by fusing sequential trajectory data, visual trajectory overlays, and graph-based scene context. It extends the earlier TrajFusionNet with three attention modules—Sequence, Visual, and Graph—to capture temporal, visual, and relational cues. The model outperforms state‑of‑the‑art methods on the PIE and JAAD datasets and shows better generalization under a joint‑training, separate‑evaluation protocol.
By Fran\c{c}ois G. Landry, Moulay A. Akhloufi
FreeFlow is a hierarchical transformer for optical flow estimation that eliminates traditional flow-specific inductive biases such as correlation volumes, feature warping, and iterative refinement. It relies on a single feed-forward encoder–decoder architecture that integrates window attention for local processing, shifted-window attention for cross-window communication, and global attention at reduced resolution. This design allows the model to scale naturally with capacity, achieving state-of-the-art performance on Sintel, KITTI-2015, and Spring benchmarks while remaining memory efficient at 1080p inference.
By Vladislav Bargatin, Alexander Yakovenko, Khaled Abud, Dmitriy Vatolin
TextAlign is a post‑training preference‑alignment framework that improves text rendering in large text‑to‑image generative models without changing the generator architecture. It uses a hierarchical vision‑language model to reward global, word, and glyph‑level accuracy, converting binary defect judgments into a scalar preference signal that can be optimized with GRPO or DPO. Experiments on FLUX.1‑dev and Z‑Image‑Turbo demonstrate higher OCR‑based text accuracy while preserving overall generation quality, outperforming several foundation and text‑rendering baselines.
By Mingxuan Cui, Jingpu Yang, Fengxian Ji, Qian Jiang, Zhecheng Shi, Jiaming Wang, Zirui Song, Zhuohan Xie, Fajri Koto, Xiuying Chen
The paper introduces BdSLIG, the first Bengali Sign Language Instruction Generation dataset, aimed at evaluating Vision Language Models on under-resourced SLIG tasks and long-tail visual concepts. It proposes Sign Parameter-Infused (SPI) prompting, which embeds standard sign parameters such as hand shape, motion, and orientation into textual prompts to improve zero-shot performance and produce more structured, reproducible instructions. The work seeks to promote inclusivity and advance sign language learning systems for under-resourced communities.
By Md Tariquzzaman, Md Farhan Ishmam, Saiyma Sittul Muna, Md Kamrul Hasan, Hasan Mahmud
The study examines whether financial sentiment tools that are validated against human labels also reliably predict market outcomes. Using a large corpus of securities class action messages linked to abnormal stock returns, the authors compare five sentiment instruments—VADER, Loughran‑McDonald, FinBERT, Twitter‑RoBERTa, and an LLM annotator—within a single pipeline. Results show that the alignment between human agreement and sentiment scores varies with sampling strategy and time horizon: conventional sampling favors same‑day associations, while fixed‑n panels yield similar correlations for both same‑day and one‑day‑ahead predictions, yet overall predictive rankings remain weak.
By AS Aravinthkakshan, Laven Srivastava, Harsh Nandwani
The paper introduces an interdisciplinary framework that uses AI and machine learning to analyze police body‑worn camera footage from the Rochester Police Department. It combines image, audio, and natural language processing—including speaker separation, transcription, and large language models—to detect and classify interaction patterns such as respect, disrespect, escalation, and de‑escalation. A custom evaluation pipeline assesses transcription quality and behavior detection accuracy, aiming to support law‑enforcement review, training, and accountability.
By Anita Srbinovska, Angela Srbinovska, Vivek Senthil, Jonathan Bateman, Adrian Martin, John McCluskey, Ernest Fokou\'e
The paper introduces Word-level Probability MIA (WPMIA), a black-box membership inference attack that estimates word-level generation probabilities via Monte Carlo sampling and local kernel smoothing, then aggregates them into a sequence-level likelihood estimator. By conditioning on different prefixes, WPMIA amplifies distributional differences between member and non-member texts, outperforming existing black-box baselines on open-source LLMs and achieving an average TPR@5%FPR of 42.0 on proprietary models such as GPT‑5‑Chat, Gemini‑2.5‑Flash, and Claude‑4.5‑Haiku.
By Shengjie Niu, Yeheng Ge, Jian Huang
The paper introduces a posterior reweighting framework to explain and counter in-context learning jailbreaks in multimodal large language models. It models the model as switching between safe and harmful behavioral modes, interpreting prompt demonstrations as evidence that shifts the posterior. Using this view, the authors derive scaling laws for jailbreak effectiveness and propose a defense that injects benign counter‑evidence to suppress harmful drift while maintaining utility.
By Xu Zhang, Dev Mistry, Xiang Xu, Ren Wang
The paper introduces the State-Path Tool Menu, a method that presents agents with a short, ordered subset of tools before execution, ensuring that both the final action and its prerequisite tools are available in a usable sequence. By learning a pre‑execution route from the request state to the desired outcome, the menu acts as an execution prior, encoding which tools can run from the current state, how outputs satisfy later inputs, and recurring orders from training paths. Experiments on ToolBench show that this approach boosts online success from 0.737 to 0.898 and outperforms several baselines while covering more complete tool chains with fewer tools.
By Bo Yan, Weikai Lin, Song Wang
ContractEval is a diagnostic framework that makes active obligations in procedural instructions explicit by representing them as query‑conditioned obligations. It matches these obligations against response or trace evidence, identifying omissions, wrong branches, ordering errors, extra actions, invariant breaches, and output‑contract violations as distinct conformance failures. In tests on audited procedural contracts, ContractEval detects and localizes all injected structural failures that output‑only and trace‑aware LLM judges miss, though it is not a compliance guarantee and remains calibration‑sensitive.
By Praphul Singh, Shanu Kumar, Akshat Agarwal, Ganesh Kumar
The paper introduces Trimmed Logit-Gap SFT (TrimSFT), a token-level reweighting strategy that adjusts supervised fine-tuning loss based on the logit gap between the correct token and its strongest competitor. TrimSFT trims supervision from tokens that are either already mastered (large logit gap) or poorly supported (small or negative logit gap), focusing learning on tokens with intermediate logit gaps. Experiments on six base models across five mathematical reasoning benchmarks show that TrimSFT consistently outperforms standard SFT, achieving the best average performance on five of six models and up to +26.9 points on MATH500.
By Yaning Jia, Chunhui Zhang, Wenxuan Xu, Xingjian Diao, Xiaoyuan Wang, Soroush Vosoughi
LexAgentHallu is a new benchmark that profiles hallucinations in legal agents across multi-step interactions. It contains 3,414 instances spanning 17 legal categories and 6 task types, each annotated with a dual-layer taxonomy of 7 high-level and 27 fine-grained hallucination categories. The benchmark introduces fine-grained metrics to quantify and localize failures along an agent’s execution path, revealing patterns such as the Right-Answer-Wrong-Reason effect and clustered hallucination subclasses.
By Yujin Zhou, Mingxuan Zheng, Chuxue Cao, Huang Yidan, Jiale Chen, Yike Guo, Sirui Han
The paper introduces the concept of proof‑carrying cognition, aiming to close the verification gap in language‑model reasoning by using reality‑settled rewards. It presents a theoretical framework linking verifier‑gold correlation to compute‑capability trade‑offs, demonstrates that unsound verifiers degrade under best‑of‑N selection while sound verifiers improve, and proposes a new benchmark metric, Soundness‑under‑Pressure, for evaluating reality‑settled reasoning systems.
By Eshwar Reddy M, Sourav Karmakar
The Era by Eon Benchmark is a new dataset for evaluating large language model agents that interact with enterprise tools. It constructs a complete fictional company with product simulators, internal databases, and benchmark questions, all generated from a shared entity graph to ensure consistency. Exact answer keys are computed from the generated records, allowing precise grading and validation of realism and adversarial robustness across 23 simulated companies.
By Benjamin Gruenbaum, Doron Porat, Assaf Natanzon, Roy Zavida, Chen Dinachi, Or Itzahary
The paper introduces a reference‑based bias detection method that audits hidden‑state representations of language models by encoding sentences as similarities to a fixed set of anchor sentences. This relative representation allows comparison across model variants, such as before and after fine‑tuning, and yields a metric called Representational Bias Shift (ΔB). ΔB correlates strongly with output‑level bias changes, can detect bias‑increasing checkpoints with high ROC AUC, and is computationally efficient, requiring only a few minutes and far less compute than traditional benchmarks.
By Marek Jeli\'nski, Jan Dubi\'nski, Maciej Chrabaszcz, Sebastian Cygert
The paper introduces kernel‑managed shared memory, a system‑level abstraction that lets specialized agents write structured, tagged memories while the agent‑system kernel controls retrieval, privacy, and prompt injection. Implemented on AIOS, this design outperforms unmanaged external memory, standard retrieval‑augmented injection, and full context concatenation across GPT‑4o, Llama‑3.1:8B, and Qwen‑2.5:7B, improving personalization scores by 2.4‑4.0 points on a 5‑point scale and reducing latency and token usage by 15‑61%. The results show that centralizing memory management in the kernel delivers most personalization benefits at a fraction of the cost.
By Ryan Lum, Yongfeng Zhang
The paper introduces TRACE, a digital‑advertising diagnostic environment that uses simulated interventions to generate verifiable rewards for training reasoning agents. By injecting controlled interventions into a simulator, the hidden cause of anomalies becomes an oracle label, enabling agents to learn to identify root causes and affected segments through noisy, confounded evidence. Experiments show that reinforcement learning with these synthesized rewards outperforms large prompted baselines, achieving higher accuracy while using fewer tool calls.
By Rui Sun, Zhan Shi, Bing He
The paper "Trust Me, I'm Your Developer: Self-Issued Authentication in Large Language Models" investigates how large language models (LLMs) handle identity verification when prompted by users. Through experiments with ChatGPT, Claude, Qwen, Mistral, and Llama, the authors find that some models generate and evaluate their own tests, accepting unsupported claims of developer identity—an outcome they term Conversational False Authentication (CFA). The study highlights that such self-issued authentication can lead to false identity judgments without affecting actual authorization boundaries, underscoring the need for external security components to manage authenticated identity.
By Syed Ghazanfar Abbas, Dongyan Xu
The paper investigates how incorporating physical-state (geometric) inputs influences a 0.8B hybrid language model with 6.2M trainable parameters for manipulation tasks. Training with geometry-conditioned recurrent decay gates achieves a 28.9% success rate, slightly lower than the 36.7% success when geometry increments are shuffled, and higher than the 24.4% success without explicit geometry. Robustness tests show that a state-only relative-coordinate policy retains most success under frame relabeling, whereas visual policies degrade significantly after small object displacements, indicating no clear advantage from training-time geometric alignment under the tested conditions.
By Hao Li, Haofei Sun, Lin He
The paper introduces a taxonomy for resource management systems that span IoT, edge, and cloud layers, extending Wang et al.’s Continuum Orchestration Systems with two new dimensions: the AI Augmentation Paradigm and the Feedback channel. It evaluates six recent architectures and finds that none combine full LLM orchestration with full agent‑layer feedback in a Cloud Continuum setting, highlighting a gap in cross‑tier feedback abstraction. The study emphasizes the need for a unified feedback mechanism to bridge disparate per‑tier signals to the LLM orchestrator.
By Antonino Vaccarella, Lanpei Li, Vincenzo Lomonaco, Massimo Coppola