OpenRaiser/Intern-Atlas huggingface dataset:
Identifying the Group to Intervene on to Maximise Effect Under Cross-Group Interference
Venue: arXiv cs.SI 2026
Year: 2026

In many networked systems, interventions applied to one group of units can induce substantial causal effects on another group through cross-group interference pathways. Despite its practical importance in domains such as public health, digital marketing, and social policy, the problem of identifying which intervention subset in a source group maximizes the benefit on a target group remains largely unaddressed. We formalize this problem as cross-group causal influence estimation and introduce the core-to-group causal effect (Co2G), a formally defined causal estimand that quantifies the contrast in target-group outcomes under intervention versus non-intervention on a candidate source subset. We establish the nonparametric identifiability of Co2G from observational network data using do-calculus under standard causal assumptions, and develop a graph neural network-based estimator that captures cross-group interference patterns. To navigate the combinatorial search space of candidate subsets, we propose CauMax, an uncertainty-aware causal effect maximization framework with two scalable selection algorithms: (i)CauMax-G, an iterative greedy search with Monte Carlo dropout--based lower confidence bounds, and (ii)CauMax-D, a differentiable gradient-based optimization via Gumbel-Softmax relaxation. Extensive experiments on two real-world social networks demonstrate that CauMax achieves an order-of-magnitude reduction in regret compared with structural heuristics and diffusion-based baselines, and that moderate uncertainty penalization consistently improves subset selection quality.
DIVE: Scaling Diversity in Agentic Task Synthesis for Generalizable Tool Use
Venue: arXiv cs.AI 2026
Year: 2026

Recent work synthesizes agentic tasks for post-training tool-using LLMs, yet robust generalization under shifts in tasks and toolsets remains an open challenge. We trace this brittleness to insufficient diversity in synthesized tasks. Scaling diversity is difficult because training requires tasks to remain executable and verifiable, while generalization demands coverage of diverse tool types, toolset combinations, and heterogeneous tool-use patterns. We propose DIVE, an evidence-driven recipe that inverts synthesis order, executing diverse, real-world tools first and reverse-deriving tasks strictly entailed by the resulting traces, thereby providing grounding by construction. DIVE scales structural diversity along two controllable axes, tool-pool coverage and per-task toolset variety, and an Evidence Collection--Task Derivation loop further induces rich multi-step tool-use patterns across 373 tools in five domains. Training Qwen3-8B on DIVE data (48k SFT + 3.2k RL) improves by +22 average points across 9 OOD benchmarks and outperforms the strongest 8B baseline by +68. Remarkably, controlled scaling analysis reveals that diversity scaling consistently outperforms quantity scaling for OOD generalization, even with 4x less data.
CR-Bench: Evaluating the Real-World Utility of AI Code Review Agents
Venue: arXiv cs.SE 2026
Year: 2026

Recent advances in frontier large language models have enabled code review agents that operate in open-ended, reasoning-intensive settings. However, the lack of standardized benchmarks and granular evaluation protocols makes it difficult to assess behavior of code review agents beyond coarse success metrics, particularly for tasks where false positives are costly. To address this gap, we introduce CR-Bench, a benchmarking dataset, and CR-Evaluator, a fine-grained evaluation pipeline for code review agents. Using these tools, we conduct a preliminary study evaluating both a single-shot agent and a Reflexion-based agent across two frontier models. We find that code review agents can exhibit a low signal-to-noise ratio when designed to identify all hidden issues, obscuring true progress and developer productivity when measured solely by resolution rates. Our analysis identifies the hidden trade-off between issue resolution and spurious findings, revealing a frontier that constrains effective agent design. Together, CR-Bench and CR-Evaluator provide a timely foundation for studying and developing code review agents as LLM-based systems transition from controlled benchmarks to real-world software engineering workflows.
Quality-Driven Agentic Reasoning for LLM-Assisted Software Design: Questions-of-Thoughts (QoT) as a Time-Series Self-QA Chain
Venue: arXiv cs.SE 2026
Year: 2026

Recent advances in large language models (LLMs) have accelerated AI-assisted software development, yet practical deployment remains constrained by incomplete implementations, weak modularization, and inconsistent security practices. We introduce Questions-of-Thoughts (QoT), a quality-driven inference-time scaffold that turns a user goal into (i) an ordered sequence of engineering steps and (ii) stepwise self-questioning to verify constraints and reduce omission errors, while maintaining a lightweight reasoning record that stabilizes subsequent design decisions. We evaluate QoT across three representative backend engineering domains: API Design, Data Communication, and File Systems. Each task requires multi-module decomposition and exposes standard failure modes in LLM-generated systems. To enable data-driven comparison, we score generated artifacts using an ISO/IEC-inspired quality rubric that measures Scalability, Completeness, Modularity, and Security. We report domain-wise gains as the change in total quality score, defined as the QoT score minus the NoQoT score. Results show capacity-dependent improvements: QoT yields consistent quality improvements for larger models and more complex domains, while smaller models may exhibit trade-offs under tight context and planning budgets. We release an open artifact with prompts, scoring guidelines, raw generations, and scripts that reproduce the reported tables and figures to support applied AI and data analytics research.
A Survey of Reasoning in Autonomous Driving Systems: Open Challenges and Emerging Paradigms
Venue: arXiv cs.AI 2026
Year: 2026

The development of high-level autonomous driving (AD) is shifting from perception-centric limitations to a more fundamental bottleneck, namely, a deficit in robust and generalizable reasoning. Although current AD systems manage structured environments, they consistently falter in long-tail scenarios and complex social interactions that require human-like judgment. Meanwhile, the advent of large language and multimodal models (LLMs and MLLMs) presents a transformative opportunity to integrate a powerful cognitive engine into AD systems, moving beyond pattern matching toward genuine comprehension. However, a systematic framework to guide this integration is critically lacking. To bridge this gap, we provide a comprehensive review of this emerging field and argue that reasoning should be elevated from a modular component to the system's cognitive core. Specifically, we first propose a novel Cognitive Hierarchy to decompose the monolithic driving task according to its cognitive and interactive complexity. Building on this, we further derive and systematize seven core reasoning challenges, such as the responsiveness-reasoning trade-off and social-game reasoning. Furthermore, we conduct a dual-perspective review of the state-of-the-art, analyzing both system-centric approaches to architecting intelligent agents and evaluation-centric practices for their validation. Our analysis reveals a clear trend toward holistic and interpretable "glass-box" agents. In conclusion, we identify a fundamental and unresolved tension between the high-latency, deliberative nature of LLM-based reasoning and the millisecond-scale, safety-critical demands of vehicle control. For future work, a primary objective is to bridge the symbolic-to-physical gap by developing verifiable neuro-symbolic architectures, robust reasoning under uncertainty, and scalable models for implicit social negotiation.
Task-Conditioned Routing Signatures in Sparse Mixture-of-Experts Transformers
Venue: arXiv cs.LG 2026
Year: 2026

Sparse Mixture-of-Experts (MoE) architectures enable efficient scaling of large language models through conditional computation, yet the routing mechanisms responsible for expert selection remain poorly understood. In this work, we introduce routing signatures, a vector representation summarizing expert activation patterns across layers for a given prompt, and use them to study whether MoE routing exhibits task-conditioned structure. Using OLMoE-1B-7B-0125-Instruct as an empirical testbed, we show that prompts from the same task category induce highly similar routing signatures, while prompts from different categories exhibit substantially lower similarity. Within-category routing similarity (0.8435 +/- 0.0879) significantly exceeds across-category similarity (0.6225 +/- 0.1687), corresponding to Cohen's d = 1.44. A logistic regression classifier trained solely on routing signatures achieves 92.5% +/- 6.1% cross-validated accuracy on four-way task classification. To ensure statistical validity, we introduce permutation and load-balancing baselines and show that the observed separation is not explained by sparsity or balancing constraints alone. We further analyze layer-wise signal strength and low-dimensional projections of routing signatures, finding that task structure becomes increasingly apparent in deeper layers. These results suggest that routing in sparse transformers is not merely a balancing mechanism, but a measurable task-sensitive component of conditional computation. We release MOE-XRAY, a lightweight toolkit for routing telemetry and analysis.
The Laziness of the Crowd: Effort Aversion Among Raters Risks Undermining the Efficacy of X's Community Notes Program
Venue: arXiv cs.HC 2026
Year: 2026

Crowdsourced moderation systems like Twitter/X's Community Notes program have been proposed as scalable alternatives to professional fact-checkers for combating online misinformation. While prior research has examined the effectiveness of such systems in reducing engagement with false content and their vulnerability to partisan bias, we identify a previously untested mechanism linking fact-check difficulty to systematic non-participation by crowdsourced raters. We hypothesize that claims requiring less cognitive effort to evaluate, specifically, those that are obviously false and easy to refute, are more likely to receive public notes than claims that are more plausible and require greater effort to debunk. Using eighteen months of vaccine-related Community Notes data (2,250 posts) and ratings from 382 survey participants, we show that claims perceived as more difficult to fact-check are significantly less likely to receive notes that achieve ``helpful''/public status. Following the conduct of additional analyses and a fact-checking process utilizing an LLM pipeline to help rule out alternative explanations, we interpret this pattern as consistent with an unwillingness among raters to invest the mental effort required to evaluate and rate notes for more plausible misinformation. These findings suggest that crowdsourced moderation may systematically fail to address the forms of plausible misinformation which are most likely to deceive. We discuss implications for platform design and propose mechanisms to mitigate this difficulty penalty in crowdsourced content moderation systems.
Uni-ASR: Unified LLM-Based Architecture for Non-Streaming and Streaming Automatic Speech Recognition
Venue: arXiv cs.SD 2026
Year: 2026

Although the deep integration of the Automatic Speech Recognition (ASR) system with Large Language Models (LLMs) has significantly improved accuracy, the deployment of such systems in low-latency streaming scenarios remains challenging. In this paper, we propose Uni-ASR, a unified framework based on LLMs that integrates both non-streaming and streaming speech recognition capabilities. We propose a joint training paradigm that enables the system to seamlessly transition between two recognition modes without any architectural modifications. Furthermore, we introduce a context-aware training paradigm and a co-designed fallback decoding strategy, which can enhance streaming recognition accuracy without introducing additional latency. The experimental results demonstrate that Uni-ASR not only achieves competitive performance within non-streaming mode, but also demonstrates strong effectiveness in streaming scenarios under diverse latency constraints.
Robust Co-design Optimisation for Agile Fixed-Wing UAVs
Venue: arXiv cs.RO 2026
Year: 2026

Co-design optimisation of autonomous systems has emerged as a powerful alternative to sequential approaches by jointly optimising physical design and control strategies. However, existing frameworks often neglect the robustness required for autonomous systems navigating unstructured, real-world environments. For agile Unmanned Aerial Vehicles (UAVs) operating at the edge of the flight envelope, this lack of robustness yields designs that are sensitive to perturbations and model mismatch. To address this, we propose a robust co-design framework for agile fixed-wing UAVs that integrates parametric uncertainty and wind disturbances directly into the concurrent optimisation process. Our bi-level approach optimises physical design in a high-level loop while discovering nominal solutions via a constrained trajectory planner and evaluating performance across a stochastic Monte Carlo ensemble using feedback LQR control. Validated across three agile flight missions, our strategy consistently outperforms deterministic baselines. The results demonstrate that our robust co-design strategy inherently tailors aerodynamic features, such as wing placement and aspect ratio, to achieve an optimal trade-off between mission performance and disturbance rejection.
Bridging Behavioral Biometrics and Source Code Stylometry: A Survey of Programmer Attribution
Venue: arXiv cs.SE 2026
Year: 2026

Programmer attribution seeks to identify or verify the author of a source code artifact using stylistic, structural, or behavioural characteristics. This problem has been studied across software engineering, security, and digital forensics, resulting in a growing and methodologically diverse set of publications. This paper presents a systematic mapping study of programmer attribution research focused on source code analysis. From an initial set of 135 candidate publications, 47 studies published between 2012 and 2025 were selected through a structured screening process. The included works are analysed along several dimensions, including authorship tasks, feature categories, learning and modelling approaches, dataset sources, and evaluation practices. Based on this analysis, we derive a taxonomy that relates stylistic and behavioural feature types to commonly used machine learning techniques and provide a descriptive overview of publication trends, benchmarks, programming languages. A content-level analysis highlights the main thematic clusters in the field. The results indicate a strong focus on closed-world authorship attribution using stylometric features and a heavy reliance on a small number of benchmark datasets, while behavioural signals, authorship verification, and reproducibility remain less explored. The study consolidates existing research into a unified framework and outlines methodological gaps that can guide future work. This manuscript is currently under review. The present version is a preprint.
GGPT: Geometry Grounded Point Transformer
Venue: arXiv cs.CV 2026
Year: 2026

Recent feed-forward networks have achieved remarkable progress in sparse-view 3D reconstruction by predicting dense point maps directly from RGB images. However, they often suffer from geometric inconsistencies and limited fine-grained accuracy due to the absence of explicit multi-view constraints. We introduce the Geometry-Grounded Point Transformer (GGPT), a framework that augments feed-forward reconstruction with reliable sparse geometric guidance. We first propose an improved Structure-from-Motion pipeline based on dense feature matching and lightweight geometric optimisation to efficiently estimate accurate camera poses and partial 3D point clouds from sparse input views. Building on this foundation, we propose a geometry-guided 3D point transformer that refines dense point maps under explicit partial-geometry supervision using an optimised guidance encoding. Extensive experiments demonstrate that our method provides a principled mechanism for integrating geometric priors with dense feed-forward predictions, producing reconstructions that are both geometrically consistent and spatially complete, recovering fine structures and filling gaps in textureless areas. Trained solely on ScanNet++ with VGGT predictions, GGPT generalises across architectures and datasets, substantially outperforming state-of-the-art feed-forward 3D reconstruction models in both in-domain and out-of-domain settings.
PACED: Distillation at the Frontier of Student Competence
Venue: arXiv cs.AI 2026
Year: 2026

Standard LLM distillation wastes compute on two fronts: problems the student has already mastered (near-zero gradients) and problems far beyond its reach (incoherent gradients that erode existing capabilities). We show that this waste is not merely intuitive but structurally inevitable: the gradient signal-to-noise ratio in distillation provably vanishes at both pass-rate extremes. This theoretical observation leads to Paced, a framework that concentrates distillation on the zone of proximal development -- the frontier of a student model's competence -- via a principled pass-rate weight $w(p) = p^α(1 - p)^β$ derived from the boundary-vanishing structure of distillation gradients. Key results: (1) Theory: We prove that the Beta kernel $w(p) = p^α(1-p)^β$ is a leading-order weight family arising from the SNR structure of distillation, and that it is minimax-robust -- under bounded multiplicative misspecification, worst-case efficiency loss is only $O(δ^2)$. (2)Distillation: On distillation from a larger teacher to a smaller student model with forward KL, Paced achieves significant gain over the base model, while keeping benchmark forgetting at a low level. (3)Self-distillation: On instruction-tuned models with reverse KL, gains are exceeding baselines as well. (4)Two-stage synergy: A forward-KL-then-reverse-KL schedule yields the strongest results in our setting, reaching substantial improvements on standard reasoning benchmarks -- supporting a mode-coverage-then-consolidation interpretation of the distillation process. All configurations require only student rollouts to estimate pass rates, need no architectural changes, and are compatible with any KL direction.
Bayesian Optimization of Partially Known Systems using Hybrid Models
Venue: arXiv cs.LG 2026
Year: 2026

Bayesian optimization (BO) has gained attention as an efficient algorithm for black-box optimization of expensive-to-evaluate systems, where the BO algorithm iteratively queries the system and suggests new trials based on a probabilistic model fitted to previous samples. Still, the standard BO loop may require a prohibitively large number of experiments to converge to the optimum, especially for high-dimensional and nonlinear systems. We present a hybrid model-based BO formulation that combines the iterative Bayesian learning of BO with partially known mechanistic physical models. Instead of learning a direct mapping from inputs to the objective, we write all known equations for a physics-based model and infer expressions for variables missing equations using a probabilistic model, in our case, a Gaussian process (GP). The final formulation then includes the GP as a constraint in the hybrid model, thereby allowing other physics-based nonlinear and implicit model constraints. This hybrid model formulation yields a constrained, nonlinear stochastic program, which we discretize using the sample-average approximation. In an in-silico optimization of a single-stage distillation, the hybrid BO model based on mass conservation laws yields significantly better designs than a standard BO loop. Furthermore, the hybrid model converges in as few as one iteration, depending on the initial samples, whereas, the standard BO does not converge within 25 for any of the seeds. Overall, the proposed hybrid BO scheme presents a promising optimization method for partially known systems, leveraging the strengths of both mechanistic modeling and data-driven optimization.
Representation Finetuning for Continual Learning
Venue: arXiv cs.LG 2026
Year: 2026

The world is inherently dynamic, and continual learning aims to enable models to adapt to ever-evolving data streams. While pre-trained models have shown powerful performance in continual learning, they still require finetuning to adapt effectively to downstream tasks. However, prevailing Parameter-Efficient Fine-Tuning (PEFT) methods operate through empirical, black-box optimization at the weight level. These approaches lack explicit control over representation drift, leading to sensitivity to domain shifts and catastrophic forgetting in continual learning scenarios. In this work, we introduce Continual Representation Learning (CoRe), a novel framework that for the first time shifts the finetuning paradigm from weight space to representation space. Unlike conventional methods, CoRe performs task-specific interventions within a low-rank linear subspace of hidden representations, adopting a learning process with explicit objectives, which ensures stability for past tasks while maintaining plasticity for new ones. By constraining updates to a low-rank subspace, CoRe achieves exceptional parameter efficiency. Extensive experiments across multiple continual learning benchmarks demonstrate that CoRe not only preserves parameter efficiency but also significantly outperforms existing state-of-the-art methods. Our work introduces representation finetuning as a new, more effective and interpretable paradigm for continual learning.
Evidential learning driven Breast Tumor Segmentation with Stage-divided Vision-Language Interaction
Venue: arXiv cs.CV 2026
Year: 2026

Breast cancer is one of the most common causes of death among women worldwide, with millions of fatalities annually. Magnetic Resonance Imaging (MRI) can provide various sequences for characterizing tumor morphology and internal patterns, and becomes an effective tool for detection and diagnosis of breast tumors. However, previous deep-learning based tumor segmentation methods have limitations in accurately locating tumor contours due to the challenge of low contrast between cancer and normal areas and blurred boundaries. Leveraging text prompt information holds promise in ameliorating tumor segmentation effect by delineating segmentation regions. Inspired by this, we propose text-guided Breast Tumor Segmentation model (TextBCS) with stage-divided vision-language interaction and evidential learning. Specifically, the proposed stage-divided vision-language interaction facilitates information mutual between visual and text features at each stage of down-sampling, further exerting the advantages of text prompts to assist in locating lesion areas in low contrast scenarios. Moreover, the evidential learning is adopted to quantify the segmentation uncertainty of the model for blurred boundary. It utilizes the variational Dirichlet to characterize the distribution of the segmentation probabilities, addressing the segmentation uncertainties of the boundaries. Extensive experiments validate the superiority of our TextBCS over other segmentation networks, showcasing the best breast tumor segmentation performance on publicly available datasets.