OpenRaiser/Intern-Atlas huggingface dataset:
Beyond Blind Spots: Analytic Hints for Mitigating LLM-Based Evaluation Pitfalls
Venue: arXiv cs.SE 2026
Year: 2026

Large Language Models are increasingly deployed as judges (LaaJ) in code generation pipelines. While attractive for scalability, LaaJs tend to overlook domain specific issues raising concerns about their reliability in critical evaluation tasks. To better understand these limitations in practice, we examine LaaJ behavior in a concrete industrial use case: legacy code modernization via COBOL code generation. In this setting, we find that even production deployed LaaJs can miss domain critical errors, revealing consistent blind spots in their evaluation capabilities. To better understand these blind spots, we analyze generated COBOL programs and associated LaaJs judgments, drawing on expert knowledge to construct a preliminary taxonomy. Based on this taxonomy, we develop a lightweight analytic checker tool that flags over 30 domain specific issues observed in practice. We use its outputs as analytic hints, dynamically injecting them into the judges prompt to encourage LaaJ to revisit aspects it may have overlooked. Experiments on a test set of 100 programs using four production level LaaJs show that LaaJ alone detects only about 45-63% of the errors present in the code (in all judges we tested), while the analytic checker alone lacks explanatory depth. When combined, the LaaJ+Hints configuration achieves up to 74% coverage (for the best performing judge and injection prompt) and produces qualitatively richer, more accurate explanations, demonstrating that analytic-LLM hybrids can substantially enhance evaluation reliability in deployed pipelines. We release the dataset and all used prompts.
Agent Tools Orchestration Leaks More: Dataset, Benchmark, and Mitigation
Venue: arXiv cs.CR 2026
Year: 2026

Driven by Large Language Models, the single-agent, multi-tool architecture has become a popular paradigm for autonomous agents. However, this architecture introduces a severe privacy risk, which we term Tools Orchestration Privacy Risk (TOP-R): an agent, to achieve a benign user goal, autonomously aggregates non-sensitive fragments from multiple tools and synthesizes unexpected sensitive information. We provide the first systematic study of this risk. We establish a formal framework characterizing TOP-R through three necessary conditions -- conclusion sensitivity, single-source non-inferability, and compositional inferability. We construct TOP-Bench via a Reverse Inference Seed Expansion (RISE) pipeline, incorporating paired social-context scenarios for diagnostic analysis. We further introduce the H-Score, a harmonic mean of task completion and safety, to quantify the utility-safety trade-off. Evaluation of six state-of-the-art LLMs reveals pervasive risk: the average Overall Leakage Rate reaches 62.11% with an H-Score of only 52.90%. Our experiments identify three root causes: deficient spontaneous privacy awareness, reasoning overshoot, and inference inertia. Guided by these findings, we propose three complementary mitigation strategies targeting the output, reasoning, and review stages of the agent pipeline; the strongest configuration, Dual-Constraint Privacy Enhancement, achieves an H-Score of 79.20%. Our work reveals a new risk class in tool-using agents, analyzes leakage causes, and provides practical mitigation strategies.
Hacking Neural Evaluation Metrics with Single Hub Text
Venue: arXiv cs.CL 2026
Year: 2026

Strongly human-correlated evaluation metrics serve as an essential compass for the development and improvement of generation models and must be highly reliable and robust. Recent embedding-based neural text evaluation metrics, such as COMET for translation tasks, are widely used in both research and development fields. However, there is no guarantee that they yield reliable evaluation results due to the black-box nature of neural networks. To raise concerns about the reliability and safety of such metrics, we propose a method for finding a single adversarial text in the discrete space that is consistently evaluated as high-quality, regardless of the test cases, to identify the vulnerabilities in evaluation metrics. The single hub text found with our method achieved 79.1 COMET% and 67.8 COMET% in the WMT'24 English-to-Japanese (En--Ja) and English-to-German (En--De) translation tasks, respectively, outperforming translations generated individually for each source sentence by using M2M100, a general translation model. Furthermore, we also confirmed that the hub text found with our method generalizes across multiple language pairs such as Ja--En and De--En.
Online Resource Allocation via Static Bundle Pricing
Venue: arXiv cs.GT 2026
Year: 2026

Online Resource Allocation addresses the problem of efficiently allocating limited resources to buyers with incomplete knowledge of future requests. In our setting, buyers arrive sequentially requesting a set of items, each with a value drawn from a known distribution. We study the efficiency of static and anonymous bundle pricing in environments where the buyers' valuations exhibit strong complementarities. In such settings, standard item pricing fails to leverage item multiplicities, while static bundle pricing mechanisms are only known for very restricted domains and their analysis relies on domain-specific arguments. We develop a unified bundle pricing framework for online resource allocation in three well-studied domains with complementarities: (i) single-minded combinatorial auctions with maximum bundle size $d$; (ii) general single-minded combinatorial auctions; and (iii) network routing, where each buyer aims to route a unit of flow from a source node $s$ to a target node $t$ in a capacitated network. Our approach yields static and anonymous bundle pricing mechanisms whose performance improves exponentially with item multiplicity. For the $d$-single-minded setting with minimum item multiplicity $B$, we obtain an $O(d^{1/B})$-competitive mechanism. For general single-minded combinatorial auctions and online network routing, we obtain $O(m^{1/(B+1)})$-competitive mechanisms, where $m$ is the number of items. We complement these results with information-theoretic lower bounds. We show that no online algorithm can achieve a competitive ratio better than $ \widetildeΩ(m^{1/(B+2)})$ for single-minded combinatorial auctions and $ \widetildeΩ(d^{1/(B+1)})$ for the $d$-single-minded setting. Our constructions exploit a deep connection to the extremal combinatorics problem of determining the maximum number of qualitatively independent partitions of a ground set.
Learning Confidence Ellipsoids and Applications to Robust Subspace Recovery
Venue: arXiv cs.DS 2026
Year: 2026

We study the problem of finding confidence ellipsoids for an arbitrary distribution in high dimensions. Given samples from a distribution $\mathcal{D}$ and a confidence parameter $α$, the goal is to find the smallest volume ellipsoid $E$ which has probability mass $\Pr_{\mathcal{D}}[E] \ge 1-α$. Ellipsoids are a highly expressive class of confidence sets as they can capture correlations in the distribution, and can approximate any convex set. This problem has been studied in many different communities. In statistics, this is the classic minimum volume estimator introduced by Rousseeuw as a robust non-parametric estimator of location and scatter. However in high dimensions, it becomes NP-hard to obtain any non-trivial approximation factor in volume when the condition number $β$ of the ellipsoid (ratio of the largest to the smallest axis length) goes to $\infty$. This motivates the focus of our paper: can we efficiently find confidence ellipsoids with volume approximation guarantees when compared to ellipsoids of bounded condition number $β$? Our main result is a polynomial time algorithm that finds an ellipsoid $E$ whose volume is within a $O(β)^{γd}$ multiplicative factor of the volume of best $β$-conditioned ellipsoid while covering at least $1-O(α/γ)$ probability mass for any $γ\in (0,1)$. We complement this with a computational hardness result that shows that such a dependence seems necessary up to constants in the exponent. The algorithm and analysis uses the rich primal-dual structure of the minimum volume enclosing ellipsoid and the geometric Brascamp-Lieb inequality. As a consequence, we obtain the first polynomial time algorithm with approximation guarantees on worst-case instances of the robust subspace recovery problem.
In-Context Algebra
Venue: arXiv cs.CL 2026
Year: 2026

We investigate the mechanisms that arise when transformers are trained to solve arithmetic on sequences where tokens are variables whose meaning is determined only through their interactions in-context. While prior work has studied transformers in settings where the answer relies on fixed parametric or geometric information encoded in token embeddings, we devise a new in-context reasoning task where the assignment of tokens to specific algebraic elements varies from one sequence to another. Despite this challenging setup, transformers achieve near-perfect accuracy on the task and even generalize to unseen groups. We develop targeted data distributions to create causal tests of a set of hypothesized mechanisms, and we isolate three mechanisms models consistently learn: commutative copying where a dedicated head copies answers, identity element recognition that distinguishes identity-containing facts, and closure-based cancellation that tracks group membership to constrain valid answers. Our findings show that the kinds of reasoning strategies learned by transformers are dependent on the task structure and that models can develop symbolic reasoning mechanisms when trained to reason in-context about variables whose meanings are not fixed.
InfoTok: Adaptive Discrete Video Tokenizer via Information-Theoretic Compression
Venue: arXiv cs.CV 2026
Year: 2026

Accurate and efficient discrete video tokenization is essential for long video sequences processing. Yet, the inherent complexity and variable information density of videos present a significant bottleneck for current tokenizers, which rigidly compress all content at a fixed rate, leading to redundancy or information loss. Drawing inspiration from Shannon's information theory, this paper introduces InfoTok, a principled framework for adaptive video tokenization. We rigorously prove that existing data-agnostic training methods are suboptimal in representation length, and present a novel evidence lower bound (ELBO)-based algorithm that approaches theoretical optimality. Leveraging this framework, we develop a transformer-based adaptive compressor that enables adaptive tokenization. Empirical results demonstrate state-of-the-art compression performance, saving 20% tokens without influence on performance, and achieving 2.3x compression rates while still outperforming prior heuristic adaptive approaches. By allocating tokens according to informational richness, InfoTok enables a more compressed yet accurate tokenization for video representation, offering valuable insights for future research.
Universal consistency of the $k$-NN rule in metric spaces and Nagata dimension. III
Venue: arXiv cs.LG 2026
Year: 2026

We establish the last missing link allowing to describe those complete separable metric spaces $X$ in which the $k$ nearest neighbour classifier is universally consistent, both in combinatorial terms of dimension theory and via a fundamental property of real analysis. The following are equivalent: (1) The $k$-nearest neighbour classifier is universally consistent in $X$, (2) The strong Lebesgue--Besicovitch differentiation property holds in $X$ for every locally finite Borel measure, (3) $X$ is sigma-finite dimensional in the sense of Jun-Iti Nagata. The equivalence (2)$\iff$(3) was announced by Preiss (1983), while a detailed proof of the implication (3)$\Rightarrow$(2) has only appeared in Assouad and Quentin de Gromard (2006). The implication (2)$\Rightarrow$(1) was established by Cérou and Guyader (2006). We prove the implication (1)$\Rightarrow$(3). We further show that the weak (instead of strong) Lebesgue--Besicovitch property is insufficient for the consistency of the $k$-NN rule, as witnessed, for example, by the Heisenberg group (here we correct a wrong claim made in the previous article (Kumari and Pestov 2024)). A bit counter-intuitively, there is a metric on the real line uniformly equivalent to the usual distance but under which the $k$-NN classifier fails. Finally, another equivalent condition that can be added to the above is the Cover--Hart property: (4) the error of the $1$-nearest neighbour classifier is asymptotically at most twice as bad as the Bayes error.
Value Under Ignorance in Universal Artificial Intelligence
Venue: arXiv cs.AI 2026
Year: 2026

We generalize the AIXI reinforcement learning agent to admit a wider class of utility functions. Assigning a utility to each possible interaction history forces us to confront the ambiguity that some hypotheses in the agent's belief distribution only predict a finite prefix of the history, which is sometimes interpreted as implying a chance of death equal to a quantity called the semimeasure loss. This death interpretation suggests one way to assign utilities to such history prefixes. We argue that it is as natural to view the belief distributions as imprecise probability distributions, with the semimeasure loss as total ignorance. This motivates us to consider the consequences of computing expected utilities with Choquet integrals from imprecise probability theory, including an investigation of their computability level. We recover the standard recursive value function as a special case. However, our most general expected utilities under the death interpretation cannot be characterized as such Choquet integrals.
Smoothing DiLoCo with Primal Averaging for Faster Training of LLMs
Venue: arXiv cs.LG 2026
Year: 2026

We propose Generalized Primal Averaging (GPA), an extension of Nesterov's method that unifies and generalizes recent averaging-based optimizers like single-worker DiLoCo and Schedule-Free, within a non-distributed setting. While DiLoCo relies on a memory-intensive two-loop structure to periodically aggregate pseudo-gradients using Nesterov momentum, GPA eliminates this complexity by decoupling Nesterov's interpolation constants to enable smooth iterate averaging at every step. Structurally, GPA resembles Schedule-Free but replaces uniform averaging with exponential moving averaging. Empirically, GPA consistently outperforms single-worker DiLoCo and AdamW with reduced memory overhead. GPA achieves speedups of 8.71%, 10.13%, and 9.58% over the AdamW baseline in terms of steps to reach target validation loss for Llama-160M, 1B, and 8B models, respectively. Similarly, on the ImageNet ViT workload, GPA achieves speedups of 7% and 25.5% in the small and large batch settings respectively. Furthermore, we prove that for any base optimizer with $O(\sqrt{T})$ regret, where $T$ is the number of iterations, GPA matches or exceeds the original convergence guarantees depending on the interpolation constants.
A Comparative Analysis of Instagram and TikTok as Islamic da'wahh Media in the Digital Era
Venue: arXiv cs.SI 2026
Year: 2026

This research aims to analyze and compare the effectiveness of Islamic da'wahh on two popular social me dia platforms, namely Instagram and TikTok. The analysis is conducted based on four main aspects: media characteristics, da'wahh communication strategies, audience engagement effec tiveness, and user behavioral responses. The research employs a a mixed-methods approach integrating qualitative content analysis and descriptive quantitative metrics through observations of the popular da'wahh account @hananattakistory during the period of October to November 2025. The findings indicate that TikTok excels in the effectiveness of disseminating da'wahh messages through high interaction rates, achieving an engagement rate of 1.42%. In contrast, Instagram records a higher total interac tion with an engagement rate of 5.47%, reflecting deeper and more reflective audience involvement. Qualitative analysis shows that TikTok is more effective in the initial stage of capturing audience attention (awareness), while Instagram is stronger in building loyalty and fostering a digital da'wahh community. Thus, combining the use of both platforms can serve as a complementary digital da'wahh strategy, expanding reach while deepening the understanding of Islamic values in the era of social media. The main contribution of this study lies in its comparative, empirically grounded approach that integrates both qualitative and quantitative analyses to map the effectiveness of da'wahh across platforms, providing a strategic foundation for developing adaptive and contextual digital da'wahh for young Muslim audiences.
In Times of Crisis: An Exploratory Study of Media and Political Discourse on YouTube During the 2024 French Elections
Venue: arXiv cs.CY 2026
Year: 2026

YouTube has emerged as a major platform for political communication and news dissemination, particularly during high-stakes electoral periods. In the context of the 2024 European Parliament and French legislative elections, this study investigates how political actors and news media used YouTube to shape public discourse. We analyze over 100,000 video transcripts and metadata from 74 French YouTube channels operated by national news outlets, local media, and political figures. To identify the key themes emphasized during the campaign period, we applied a semi-automated method that combined large language models with clustering and manual review. The results reveal distinct thematic patterns across the political spectrum and media types, with right-leaning news outlets focusing on topics like immigration, while left-leaning emphasized protest and media freedom. Themes generating the most audience engagement, measured by comment-to-view ratios, were most often the most polarizing ones. In contrast, less polarizing themes such as video games and nature showed higher approval, reflected in like-to-view ratios. We also observed a general tendency across all media types to portray political figures in neutral or critical terms rather than favorable ones.
Exploiting ID-Text Complementarity via Ensembling for Sequential Recommendation
Venue: arXiv cs.LG 2026
Year: 2026

Modern Sequential Recommendation (SR) models commonly utilize modality features to represent items, motivated in large part by recent advancements in language and vision modeling. To do so, several works completely replace ID embeddings with modality embeddings, claiming that modality embeddings render ID embeddings unnecessary because they can match or even exceed ID embedding performance. On the other hand, many works jointly utilize ID and modality features, but posit that complex fusion strategies, such as multi-stage training and/or intricate alignment architectures, are necessary for this joint utilization. However, underlying both these lines of work is a lack of understanding of the complementarity of ID and modality features. In this work, we address this gap by studying the complementarity of ID- and text-based SR models. We show that these models do learn complementary signals, meaning that either should provide performance gain when used properly alongside the other. Motivated by this, we propose a new SR method that preserves ID-text complementarity through independent model training, then harnesses it through a simple ensembling strategy. Despite this method's simplicity, we show it outperforms several competitive SR baselines, implying that both ID and text features are necessary to achieve state-of-the-art SR performance but complex fusion architectures are not.
ShareChat: A Dataset of Chatbot Conversations in the Wild
Venue: arXiv cs.CL 2026
Year: 2026

While academic research typically treats Large Language Models (LLM) as generic text generators, they are distinct commercial products with unique interfaces and capabilities that fundamentally shape user behavior. Current datasets obscure this reality by collecting text-only data through uniform interfaces that fail to capture authentic chatbot usage. To address this limitation, we present ShareChat, a large-scale corpus of 142,808 conversations (660,293 turns) sourced directly from publicly shared URLs on ChatGPT, Perplexity, Grok, Gemini, and Claude. ShareChat distinguishes itself by preserving native platform affordances, such as citations and thinking traces, across a diverse collection covering 101 languages and the period from April 2023 to October 2025. Furthermore, ShareChat offers substantially longer context windows and greater interaction depth than prior datasets. To illustrate the dataset's breadth, we present three case studies: a completeness analysis of intent satisfaction, a citation study of model grounding, and a temporal analysis of engagement rhythms. This work provides the community with a vital and timely resource for understanding authentic user-LLM chatbot interactions in the wild. The dataset is publicly available via Hugging Face.
Your Eyes Controlled the Game: Real-Time Cognitive Training Adaptation based on Eye-Tracking and Physiological Data in Virtual Reality
Venue: arXiv cs.HC 2026
Year: 2026

Cognitive training for sustained attention and working memory is vital across domains relying on robust mental capacity such as education or rehabilitation. Adaptive systems are essential, dynamically matching difficulty to user ability to maintain engagement and accelerate learning. Current adaptive systems often rely on simple performance heuristics or predict visual complexity and affect instead of cognitive load. This study presents the first implementation of real-time adaptive cognitive load control in Virtual Reality cognitive training based on eye-tracking and physiological data. We developed a bidirectional LSTM model with a self-attention mechanism, trained on eye-tracking and physiological (PPG, GSR) data from 74 participants. We deployed it in real-time with 54 participants across single-task (sustained attention) and dual-task (sustained attention + mental arithmetic) paradigms. Difficulty was adjusted dynamically based on participant self-assessment or model's real-time cognitive load predictions. Participants showed a tendency to estimate the task as too difficult, even though they were objectively performing at their best. Over the course of a 10-minute session, both adaptation methods converged at equivalent difficulty in single-task scenarios, with no significant differences in subjective workload or game performance. However, in the dual-task conditions, the model successfully pushed users to higher difficulty levels without performance penalties or increased frustration, highlighting a user tendency to underestimate capacity under high cognitive load. Findings indicate that machine learning models may provide more objective cognitive capacity assessments than self-directed approaches, mitigating subjective performance biases and enabling more effective training by pushing users beyond subjective comfort zones toward physiologically-determined optimal challenge levels.