Artificial Intelligence
AgentOmnia: Scaling Agentic Models for Full-Scenario Applications
Hao Jiang, Gangtao Xin, Yingdi Huang et al. (+35 more)
Large
language model
agents have advanced rapidly, yet progress remains fragmented across domains, capabilities, task difficulty, and interaction settings. We frame this as full-scenario agentic scaling and present AgentOmnia, a framework coordinating task-space definition, data synthesis, post-training, evaluation, and improvement across To-Consumer (ToC), To-Business (ToB), and To-Employee (ToE) applications. An extensible Domain x Capability x Atomic Difficulty taxonomy aligns these stages and enables fine-grained diagnosis with OmniaBench. AgentOmnia combines bidirectional environment-task synthesis with tool-dependency, program-structured, and solver-based pipelines, constructing 5,018 stateful environments with 255,375 tools and 52,361 tasks. Programs, solvers, and verifiers provide correctness signals, while supervised fine-tuning, online agentic reinforcement learning, and a rollback curriculum support post-training. Evaluation failures translate into Product Requirement Documents (PRDs) for targeted self-
evolution. Starting from Qwen3-30B-A3B-Thinking-2507, AgentOmnia raises the pass rate on the OmniaBench challenging subset from 9.16% to 37.11% and the macro-average across OmniaBench, $\tau^2$-Bench, DeepPlanning, and VitaBench from 22.86% to 41.69%. Under a unified protocol,it leads the evaluated agentic post-trained baselines on OmniaBench and retains the highest four-
benchmark macro-average. It also surpasses Qwen3-235B-A22B-Thinking-2507 on all four
benchmarks and exceeds Qwen3.5-35B-A3B on the macro-average. Gains span three application splits, ten capability dimensions, eight atomic-difficulty factors, and 76 of 90 level-1 domains, indicating broad rather than category-specific improvement. A one-round study provides initial evidence for PRD-guided self-
evolution, motivating validation at larger scales and in industrial settings.
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All authors Hao Jiang, Gangtao Xin, Yingdi Huang, Guojie Zhu, Jiangshan Zhang, Xinyuan Lin, Yunkun Xu, Chengyu Shen, Wenlong Fei, Jiawei Li, Yujie Fu, Sichen Kang, Tingyu Xie, Yedi Hu, Jingren Zhang, Hongcheng Gao, Jianshu Zeng, Chong Chen, Chang Guo, Chao Feng, Feng Wang, Fulin Lin, Jinchao Ma, Lang Mei, Li Huang, Liyan Liu, Qing He, Shuting Tao, Siyu Mo, Xiangnan Chen, Xiaohan Yu, Xiaoyang Li, Yanheng Hou, Yanyu Wu, Zhihan Yang, Wentao Zhang, Yang Gao, Zhao Cao
Artificial Intelligence
SQBench: A Benchmark for Evaluating Task Delivery by Language-Model Agents in Production-Oriented Workflows
Summer Sun
Existing evaluations of large
language models cover knowledge,
reasoning, coding, and tool use, but they rarely treat a verifiable deliverable produced within a constrained workflow as the unit of evaluation. We introduce SQBench, a
benchmark for evaluating production-oriented task delivery by
language-model
agents. SQBench v1.0 contains 220 standardized tasks organized into L1 atomic capabilities, L2 composite skills, and L3 business scenarios. Each task requires an
agent to process input assets, use available tools, and produce an explicitly specified deliverable. The evaluation first computes functional Completion and then derives Risk Penalty and Performance from independently evidenced triggers in a 10D Risk
Matrix. A Strict Pass requires Completion = 1 and Risk Penalty = 0. We evaluate 27 model configurations under a common protocol, with one run per configuration-task pair. The highest prespecified Weighted Pass@1 is 60.5%. Mean Strict Pass@1 on L3 is 18.5%, and every configuration performs worse on L3 than on both L1 and L2, indicating that delivery under domain constraints is a shared weakness within the current task set. Of 2,348 results with Completion = 1, 113 (4.8%) fail the Strict Pass criterion because of risks such as unverifiable citations, inappropriate resource use, or format violations. These results show that functional completion alone does not fully characterize delivery quality and that risk determinations should be reported separately.
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Artificial Intelligence · Cross-listed from cs.CE
A scalable online machine learning approach for Stock Recommendation
Harsh Nagarkar
Stock recommendation systems face the dual challenge of adapting to rapidly changing
market conditions while maintaining low-latency predictions for end users. Traditional batch-trained models fail to capture concept drift, and monolithic architectures struggle to provide fault tolerance under load. This paper presents a scalable online deep learning-based stock recommendation system built on a distributed microservices architecture using Kubernetes, Docker, and RabbitMQ. The system employs a hybrid leader-follower architecture where a primary model continuously trains on streaming financial data, including EPS, MACD, and price, from the Alpha Vantage API while multiple replica models serve user-facing recommendations in parallel. A multilayer perceptron implemented with TensorFlow Recommenders generates content-based recommendations using explicit user ratings (1-5) and transfer learning. The architecture ensures high availability. The leader persists model weights to Google Cloud Object Storage, allowing replicas to recover seamlessly upon failure, while RabbitMQ provides message durability and replay. Results demonstrate that the system serves stock recommendations in 23 seconds per request and processes up to 500 portfolio addition requests per second per follower. Key limitations include data staleness (up to 150 minutes due to API rate limits) and the absence of a service mesh for inter-cluster security. This work contributes a production-ready reference architecture for online recommender systems that balances consistency, availability, and scalability in a financial domain context
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Artificial Intelligence · Cross-listed from math.OC
KAYROS: An Anytime and Exact Open-Source Solver for Duration-Minimization Time-Dependent Vehicle Routing. A Technical Report and a Case Study in Human-AI Engineering
Florian Rascoussier, Romain Billot, Lina Fahed et al. (+1 more)
Time-dependent routing recognizes that the same journey can take a different time depending on when it begins. Under duration minimization, even the departure time of each vehicle becomes a decision. Exact methods for this setting exist in the literature, but researchers and practitioners have lacked a ready-to-use open solver that combines rich piecewise-linear travel times, early feasible solutions and optimality claims. KAYROS fills this gap with two modes on one checker-consistent engine: an Iterated Local Search that streams improving solutions and a Branch-Price-and-Cut method that can issue computational optimality certificates under explicit arithmetic and search assumptions. It installs with one command and has no proprietary dependency. The public MAMUT-routing store currently contains 704 KAYROS certificates under a four-solve publication protocol, which has also led to the retraction and repair of invalid earlier claims. The report presents two complementary
benchmark contributions to MAMUT-routing. The first integrates Blauth2024, a
benchmark from the literature whose travel times derive from measured Uber speeds, for which KAYROS provides new best-known solutions on all 40 instances. The second proposes Poryos2026, a new
benchmark of 1,080 paired static and time-dependent instances built from OpenStreetMap road networks and controlled synthetic traffic. Finally, the report describes the intensive human-AI collaboration behind this work and the verification practices that kept its outputs independently verifiable.
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All authors Florian Rascoussier, Romain Billot, Lina Fahed, Christine Solnon
Artificial Intelligence
Compiler-Grounded Hierarchical Diagnosis for LLM-Based Triton Kernel Optimization
Dongjie Chen, Ping Zhao, Bohua Zhan et al. (+11 more)
Recent advances in large
language models (LLMs) have enabled automated kernel generation and optimization, but most existing approaches rely on surface signals such as compilation feedback and profiling metrics. These signals reveal that a kernel is slow, but not why the backend compiler fails to realize a profitable optimization, especially on emerging accelerators such as NPUs. We therefore formulate kernel optimization as a progressive cross-layer diagnosis problem that links runtime symptoms to IR structure and compiler behavior before rewriting source. Based on this insight, we present our system, a compiler-grounded and hierarchical optimization framework for Triton kernels. the system escalates from lightweight pattern triage and profiling diagnosis to IR attribution and compiler-grounded analysis only when deeper evidence is needed, then proposes evidence-backed source-level rewrites. We implement the system on Triton for Ascend NPUs and evaluate it on 37 successfully converted entries from a standardized NPUKernelBench-derived Ascend 950
benchmark. Across these entries, the system attains a
geometric-mean speedup of 4.35$\times$ and a median speedup of 2.73$\times$ from the initial to optimized Triton kernel; 22/37 exceed 2$\times$ and 13/37 exceed 5$\times$. The complete distribution ranges from near-baseline entries to large wins, motivating transparent reporting of the current system's scope and limitations.
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All authors Dongjie Chen, Ping Zhao, Bohua Zhan, Yulong Wang, Shushu Chen, Liangjun Feng, Hao Zhou, Min Shen, Linmu Wang, Weijia Sheng, Xiangyu Wei, Weijie Ding, Jianhui Huang, Yaoqing Gao
Artificial Intelligence · Cross-listed from cs.CR
Poster: Rethinking Security in LLM Code Generation through Real-World Risk Scenarios
Lixun Ma, Ruolong Ma, Bei Wang et al. (+4 more)
Large
Language Models (LLMs) are widely used for code generation, yet their security behavior in realistic development workflows remains underexplored. Existing
benchmarks often rely on explicitly specified security requirements, failing to capture real-world scenarios where
prompts are frequently ambiguous or incomplete. In this paper, we adopt a developer-centric perspective and identify three representative risk scenarios that commonly lead to security vulnerabilities in LLM-generated code: Ambiguous Requirements, Under-Specified Operational Context, and Security–Functionality Conflict. Based on these scenarios, we construct a large-scale
benchmark comprising 2,700 test cases, enabling fine-grained evaluation of LLM security under realistic conditions. Extensive evaluation of eight state-of-the-art LLMs reveals that all models exhibit average vulnerability rates exceeding 56% across risk scenarios. We further demonstrate that security-aware prompting can substantially mitigate these risks, achieving up to 45% improvement.
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All authors Lixun Ma, Ruolong Ma, Bei Wang, Feng Wei, Zhenguang Liu, Lorenzo Cavallaro, Wentao Chen
Artificial Intelligence · Cross-listed from cs.SI
Scoping Review of AI, Metrology, and ESG in the Semiconductor Sector: Implications for Safe and Sustainable by Design (SSbD)
Karen Ang and Han-Teng Liao
The
semiconductor sector faces a dual transition: scaling manufacturing execution through Artificial Intelligence (AI) while satisfying stringent sustainability mandates, such as the EU Carbon Border Adjustment Mechanism (CBAM). This paper presents a scoping review of 1,465 documents indexed in Web of Science and Scopus, spanning AI-integrated metrology, supply chain ESG, and
federated industrial data spaces. Network analysis reveals a highly fragmented "core-periphery" knowledge structure, emphasizing a critical structural hole between AI-driven process optimization and downstream sustainability governance. To close these gaps, this study proposes a 6-layer Safe and Sustainable by Design (SSbD) architecture grounded in a System of Systems (SoS) paradigm. By establishing distinct "grid-to-core" and "standards-through-supply-chain" integration pathways, the proposed framework demonstrates how virtual metrology (VM), localized
federated learning, and defensive RegTech mechanisms can build provenance-aware data fabrics. Ultimately, this architecture positions regulatory compliance as a driver for innovation, enabling secure,
climate-neutral, and circular value chains in
semiconductor manufacturing.
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Artificial Intelligence
Structure over Depth: A Single-Block Spatio-Temporal Transformer for Multi-Entity Reasoning
Narthana Sivalingam, Santhirarajah Sivasthigan, Buddhi Wijenayake et al. (+3 more)
Modeling multi-entity temporal data requires capturing dependencies across entities, time, and their interactions.
Transformer-based approaches perform well but often rely on deep stacks of layers to learn these heterogeneous dependencies implicitly, increasing computational cost. We revisit this problem from a structural perspective and decompose multi-entity temporal dynamics into three interaction types: spatial interactions among entities, temporal interactions across time, and cross interactions coupling the two domains. We propose a structured spatio-temporal
transformer block that explicitly models all three within a single stage. It uses parallel spatial and temporal self-
attention, followed by bidirectional cross-
attention, and combines the outputs through learnable gated fusion. By directly encoding these complementary views, the model reduces the need for deep stacking. We evaluate the approach on
video-based group activity recognition, skeleton-based human interaction analysis, and wearable sensor-based activity recognition. Despite its simplicity, the single structured
Transformer block matches or outperforms deeper architectures with only 1.76M parameters. The results suggest that depth in prior models partly compensates for implicit and entangled interaction modeling, whereas explicit factorization offers a more efficient and transparent alternative. More broadly, this work supports a structure-first design principle: expressive multi-entity temporal
reasoning can emerge by exposing interaction structure rather than relying on depth.
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All authors Narthana Sivalingam, Santhirarajah Sivasthigan, Buddhi Wijenayake, Roshan Godaliyadda, Vijitha Herath, Parakrama Ekanayake
Artificial Intelligence · Cross-listed from cs.CR
Traceable LLM Reasoning for Fake-Order Fraud Detection
Siqi You, Bingsong Xu, Zhixian Zheng et al. (+4 more)
Detecting fake-order fraud at scale remains a critical challenge for large online-to-offline (O2O) service platforms, as existing approaches often rely on expert-designed features, produce black-box decisions, and provide limited interpretability. To address these limitations, we propose DeepScrub, a reinforcement learning framework built upon large
language models (LLMs) for fake-order fraud detection with traceable
reasoning. DeepScrub introduces three innovations. First, a semantic unification module converts heterogeneous risk signals into textual descriptions that LLMs can understand. Second, continued pre-training on risk-control corpora injects domain knowledge, and task rewards jointly evaluate prediction correctness and
reasoning quality. Third, the SUggest-REflect (SURE) mechanism incorporates expert feedback and model self-checking to iteratively refine
reasoning paths. On a real-world fake-order fraud detection
dataset, DeepScrub achieves a macro-F1 score of 85.3%, outperforming the best baseline by 2.7 percentage points. Our task-optimized 8B model further surpasses a 32B model, showing that domain adaptation can matter more than model scale in this setting. In a four-week live pilot, DeepScrub achieved 91.8% precision and 88.5% recall, improving over first-stage human reviewers by 16.6 and 38.8 percentage points. It reduced first-stage manual review workload by 94% and saved nearly one million RMB annually. These results show that DeepScrub improves fraud review accuracy, reduces first-stage review workload, and provides traceable evidence for production risk-review workflows.
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All authors Siqi You, Bingsong Xu, Zhixian Zheng, Xinjian Peng, Yang Xie, Ying Wang, Jiarong Xu
Artificial Intelligence · Cross-listed from cs.CL
Attention-Guided Layer Selection for Contrastive Decoding in Large Language Models
Yusuke Sakai, Natthawut Kertkeidkachorn and Kiyoaki Shirai
Contrastive decoding methods such as DoLa improve the factuality of Large
Language Models (LLMs) by contrasting the output distributions of mature and premature layers. However, DoLa's dynamic layer selection relies solely on divergences in output vocabulary distributions. In this work, we propose three
attention-guided strategies:
Attention-JSD,
Attention-
Entropy-Max, and
Attention-
Entropy-Min, which leverage structural information carried by internal self-
attention mechanisms as a signal for layer selection. Experimental results on TruthfulQA demonstrate that our strategies, particularly
Attention-JSD and
Attention-
Entropy-Min, consistently outperform the original DoLa. We observe significant gains on multi-answer metrics (MC2 and MC3), suggesting that
attention distributions can provide a more sensitive signal for resolving factual knowledge than output vocabulary distributions.
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Artificial Intelligence · Cross-listed from cs.CL
ADAGE: A Language-Agnostic Pipeline for Analogical Reasoning Evaluation
Ahmed Haj Ahmed and Alvin Grissom II
Multilingual reasoning evaluation overwhelmingly relies on translating English
benchmarks, a practice that introduces linguistic artifacts and fails to test culturally-grounded
reasoning. We introduce ADAGE (Analogical Difficulty-by-design Assessment for Grounded Evaluation), a
language-agnostic pipeline that combines native-speaker curation with LLM-assisted generation to construct challenging, translation-free
benchmarks for abstract analogical
reasoning. We validate ADAGE by constructing
benchmarks for Arabic, Amharic, and Japanese. Evaluating 14 open-weight models, we find a consistent cultural
reasoning gap: models that perform well on English proverb
reasoning struggle substantially on all three native
benchmarks, with accuracy dropping by 12–52 percentage points relative to English. We release the pipeline, all three
benchmarks, and the full evaluation suite.
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Artificial Intelligence
SymStep: Symbolic Step Verification for Logical Reasoning
Aida Usmanova, Rui Gao, Dilshod Azizov et al. (+2 more)
Chain-of-thought (CoT) prompting can fail severely on constraint-dense logical
reasoning tasks, where unverified errors accumulate silently across steps. We introduce SymStep: an LLM makes one atomic claim at a time (DEDUCE: Alice, pet, Cat), then a lightweight constraint propagator checks the claim for consistency with prior accepted deductions, rejects contradictions, and cascades implied facts automatically. SymStep+G additionally provides MRV guidance after each accepted step, directing the LLM toward the most constrained unresolved variable. On a 35-puzzle retained subset of ZebraLogicBench, a
benchmark of 1,000 Einstein-style logic puzzles, Direct and CoT both achieve 0%, while SymStep+G reaches 97%. On AR-LSAT analytical
reasoning problems, SymStep achieves 100% vs. CoT's 87%. On LGP-14, SymStep+G achieves 100% vs. 0% for CoT and Logic-LM, the strongest prior symbolic+LLM baseline we compare against. Ablation studies reveal that MRV guidance is a key mechanism for reducing directionless cycling, while consistency checking provides a safety net against explicit contradictions. Across six
benchmarks spanning five task domains, SymStep variants match or exceed every baseline on constraint-dense and arithmetic tasks. Experiments on AQUA-RAT
algebra confirm the advantage is constraint-density-specific.
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All authors Aida Usmanova, Rui Gao, Dilshod Azizov, Ricardo Usbeck, Zangir Iklassov
Artificial Intelligence · Cross-listed from cs.LG
Through the Bottleneck: How Multi-head Latent Attention Separates Content from Position in Language Models
Dhruvil S, Fenil Sojitra and Ravirajsinh Chauhan
Multi-head Latent
Attention (MLA), introduced in DeepSeek-V2, compresses key-value pairs through a shared low-rank bottleneck (cKV), achieving 81% KV-cache reduction during inference. Despite its adoption in massive production models, no prior work has studied what information this bottleneck preserves or discards, nor how it reshapes internal
transformer circuits. We present the first comprehensive mechanistic interpretability study of MLA, training a 114M-parameter
transformer (pretrained on a web/code/math mixture, fine-tuned on TinyStories) and analyzing its representations through SVD,
attention head taxonomy, linear probing, and a disruption-attribution analysis. Our key findings are: (1) the cKV bottleneck learns a pure content representation, preserving entity identity (98% retention) while discarding positional information, validating MLA's separation of content from position via RoPE; (2) induction heads co-locate at a single layer (Layer 12), unlike their distributed formation in standard MHA; (3) a single "semantic hub" layer (Layer 15) simultaneously exhibits the highest SVD effective rank and strongest disruption-attribution score; and (4) the bottleneck is globally over-provisioned, using only 46% of its capacity on average. These findings suggest MLA does not merely compress
attention passively, but reshapes how the model organizes content, position, and
circuit structure. We view this as an initial data point and detail scope limitations in Section 5.
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Artificial Intelligence · Cross-listed from cs.LG
MixQuant: Adaptive Mixed-Precision Quantization for Large Language Models
Ashitabh Misra, Madhav Agrawal, Arham Jain et al. (+1 more)
Mixed-precision quantization improves the accuracy of post-training quantization by allocating higher bitwidths to sensitive layers, but existing methods solve the allocation for a single fixed
memory budget. In practice the budget varies across deployments and is unknown at calibration time. Adaptive quantization addresses this with one offline calibration that serves any budget, yet current methods score layer sensitivity in a manner that does not consider its dependency on quantization levels of other layers. We show that a layer's sensitivity depends strongly on the bitwidths of its upstream layers and that this dependence shifts the resulting preferred bit allocation. We propose MixQuant, a technique-agnostic adaptive framework that wraps any base quantizer. MixQuant marginalizes each layer's distortion over random quantized upstream configurations to obtain budget-agnostic scores, calibrates the quantizer's parameters on plans the allocator itself produces, and penalizes allocations that leave layers at the lowest bitwidths. A single greedy pass then serves any budget at deployment. Across Llama-3.2-3B, Llama-2-7B, and Mistral-7B under AWQ and GPTQ, MixQuant outperforms adaptive and mixed-precision baselines in every setting, improving average accuracy by up to 8 points and reducing perplexity from 12.43 to 10.70 at the tightest budget, while matching an ILP solver at negligible deployment cost.
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All authors Ashitabh Misra, Madhav Agrawal, Arham Jain, Tarek Abdelzaher
Artificial Intelligence
Stress-testing large language model agents in a robotic chemistry laboratory
Lulu Guo, Yingkai Sun, Xiaobo Li et al. (+13 more)
AI is evaluated through knowledge,
reasoning and plan generation, yet scientific agency requires reliable physical action and adaptation to evidence. Here, we use a robotic chemistry laboratory as a physical-world testbed to make scientific agency measurable. Its 45 modular workstations exposed as machine-readable skills enabled 4,608 trials. Only 3.3% of trials produced expert-assessed executable workflows under laboratory constraints; even the best system achieved 28.1%. Long-horizon planning remained a challenge: only three executable workflows exceeded 30 operations, although the longest contained 44. Across five rounds, experimental feedback prompted local adjustments but no workflow-level replanning or analytical-method redesign. By making physical executability and evidence-driven replanning measurable, our study provides an evidence-based assessment of deployment readiness and a diagnostic framework to guide closed-loop improvements towards physically grounded autonomous research.
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All authors Lulu Guo, Yingkai Sun, Xiaobo Li, Luyao Ge, Ziming Wang, Haitao Zheng, Jingyu Li, Huijuan Zhang, Bingxu Chen, Daobin Liu, Yuebo Liu, Jie Li, Xiaohui Li, Linjiang Chen, Yi Luo, Jun Jiang
Artificial Intelligence · Cross-listed from cs.LG
Multi-Agent Privacy Game in Federated Learning: A Unified Mean-Field View
Kun Zhao and Xu Chen
Federated learning enables collaborative model training across distributed clients without centralising their data, yet
privacy remains a persistent concern because the shared model updates can leak information about local
datasets. Existing
privacy-preserving methods either inject calibrated noise into client updates, limiting their composition guarantees, or formulate client
privacy choices as a multi-
agent game whose Nash
equilibrium becomes intractable as the number of clients grows. We bridge these two lines of work by formulating
privacy-preserving
federated learning as a mean-field
privacy game: each client strategically chooses its own
privacy budget while interacting with the population only through a single mean-field statistic. The mean-field limit yields a tractable
equilibrium for arbitrarily many clients, accommodates heterogeneous client preferences, and inherits an exponentially decaying
privacy guarantee through a log-Sobolev contraction. The framework recovers the entropic
privacy baseline as the homogeneous special case and the multi-
agent privacy game as the finite-population case. Experiments on quadratic regression, logistic regression, and MNIST demonstrate that the proposed framework attains the
privacy-utility trade-off of the entropic baseline while delivering a personalized
privacy guarantee that the homogeneous baseline cannot express.
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Artificial Intelligence · Cross-listed from cs.LG
All in One: Generative Modeling as Mean-Field Game Design
Kun Zhao and Xu Chen
Mean-field games (MFGs) offer a unifying lens on continuous-time generative modeling: a cost tuple recovering twelve prominent models—Continuous Normalizing Flows, OT-Flow, Score-based Models, Schrödinger Bridges, and more—as special cases of one variational problem. Yet two dimensions of this space remain entirely unexplored: the interaction term $\mathcal{I}$ is set to zero in many existing models, and the rich family of MFG solvers has never been applied to generative modeling. We address both gaps with MFGLab an open-source PyTorch library whose primary API is the cost tuple: all twelve models are specified by four composable cost functions, and the training loop, log-Jacobian, and reverse-ODE sampler are shared automatically. We additionally propose DI-Flow, a novel cost design that uses a differentiable
entropy functional to encourage mode coverage, and provide learning-based MFG solvers that substantially outperform neural training on
stochastic-dynamics rows. Experiments on two 2-D
benchmarks confirm that the unified API is lossless relative to hand-coded implementations.
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Artificial Intelligence
Reason Popper-ly: Patching In-Context Reasoning with Inductive Logic Programming
Zirong Chen and Meiyi Ma
Chain-of-thought (CoT) prompting enables large
language models (LLMs) to tackle multi-step
reasoning tasks, yet the generated intermediate steps are not guaranteed to be logically sound. We present Reason Popper-ly, a neurosymbolic framework that uses inductive logic programming (ILP) to learn relation composition rules from
reasoning traces and deploys them as an online verifier for step-level correction. Given an LLM-generated trace, the method checks each inferred step against the learned rule table, diagnoses the violation type, rewrites incorrect steps with symbolically derived repairs, and regenerates the remaining suffix so that the model can produce its final answer conditioned on a verified trace. We evaluate on CLUTRR, a multi-hop kinship
reasoning benchmark, using five
language models over
reasoning chains of 2 to 10 hops. Across all models, Reason Popper-ly consistently improves terminal accuracy over standard CoT, with gains of up to 48 percentage points for small models and 15 points for frontier models on the longest chains. Compared with a fully exogenous symbolic pipeline, our method performs better on harder instances by preserving the model's successful grounding while correcting only verifiable
reasoning failures. In addition, step-level ILP verification yields a fine-grained error taxonomy that provides diagnostic insight beyond final-answer accuracy.
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Artificial Intelligence · Cross-listed from cs.IR
VecTree-RAG: An Agentic Retrieval-Augmented Generation Framework Combining Vector and Tree Retrieval for Efficiency and Accuracy
Xinyan Zhong, Yuwei Shi, Yuqi Wei et al. (+3 more)
Scientific question answering requires a
retrieval system to solve two distinct problems: identifying which papers are relevant and locating the supporting evidence within those papers. Conventional
retrieval-augmented generation typically addresses both through similarity search over fixed-length passages, flattening document structure and separating scientific claims from their methodological and argumentative context. We present VecTree-RAG, an agentic framework that assigns these tasks to complementary
retrieval mechanisms. Vector search ranks compact document and section representations across the corpus, whereas
reasoning-guided traversal of source-verified section trees localizes evidence within shortlisted papers. Full text is retained in a page store and exposed progressively only after structural localization. We evaluate VecTree-RAG on 300 QASPER questions, an open-access subset of 54 LitQA2 questions, and 49 multi-document MOSAIC questions. Compared with Dense RAG, reranked Dense RAG, RAPTOR, and Search-o1, VecTree-RAG obtained the highest observed answer score on all three
benchmarks, reaching 0.800 LLM-judge correctness on QASPER, 0.925 accuracy on LitQA2, and a 0.547 composite score on MOSAIC. On QASPER, its evidence-page precision was 0.274, compared with 0.046–0.071 for the baselines. LitQA2 ablations further showed that the complete vector–tree architecture required fewer inference
tokens than variants without tree
navigation or corpus-level vector routing. These results indicate that vector
retrieval narrows the corpus-level search space and tree
navigation concentrates reading on structurally relevant evidence. Although multi-turn inference remains more expensive than single-call
retrieval, VecTree-RAG provides a structure-aware and traceable architecture for scientific literature question answering.
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All authors Xinyan Zhong, Yuwei Shi, Yuqi Wei, Chen Shen, Tianhang Zhou, Zhenghao Wu
Artificial Intelligence · Cross-listed from math.CO
Exact values and exact upper bounds for families of integers with arithmetic progression intersections (Erdős Problem #272)
Zhanfu Yang
Let $t(N)$ be the largest $t$ for which there exist distinct sets $A_1,\dots,A_t \subseteq \{1,\dots,N\}$ such that $A_i \cap A_j$ is a nonempty arithmetic progression for all $i \neq j$ (Erdos Problem #272). Simonovits and Sos proved $t(N)=O(N^2)$ and conjectured $\binom{N}{2}+1$ is best possible; Szabo disproved this by a construction giving $t(N) \geq \binom{N}{2}+1+\lfloor(N-1)/4\rfloor$, proved the asymptotics $t(N)=N^2/2+O(N^{5/3}(\log N)^3)$, and asked whether $t(N)=\binom{N}{2}+O(N)$ and whether some element lies in all sets of any extremal family (the kernel question). We determine $t(N)$ exactly for all $3 \leq N \leq 12$ by exhaustive computation: in this entire range Szabo's lower bound is exact, and we
conjecture that $t(N)=\binom{N}{2}+1+\lfloor(N-1)/4\rfloor$ for every $N$. Towards the matching upper bound we prove, for every $N$, that Szabo's bound is the exact maximum over all families with a common element (starred families). The proof combines a self-contained “defect-one” counting inequality for staircase regions with a new structural
theorem: every non-progression member of such a family contains a bad pair that no other member can share. Consequently the sharpened
conjecture reduces to a single remaining statement, namely Szabo's kernel
conjecture that some element lies in all sets of an extremal family, and we prove first structural constraints on putative non-starred extremal families.
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Artificial Intelligence · Cross-listed from cs.SE
Adversarial Test-Hardening for AI-Written Code: An Instrument Autopsy and a Pre-Registered Causal Estimate of the Critic Loop
Jeff Otterson
Large
language models increasingly write both code and the tests meant to check it; coverage records what ran, not what was verified. We study an
adversarial test-hardening loop under a mechanical oracle: a Tester model writes tests, mutation testing names surviving injected defects, and a Critic model writes tests to kill exactly those, with every verdict decided mechanically, so no model judges another's output. In Experiment 1, on five Python subjects (one same-lineage-loop
cell could not be scored), the loop killed 105 mutants that one-shot generation missed and lost none, and the cross-lineage-Critic question returned a pre-declared null. The central finding was an autopsy: an earlier analysis reported a cross-lineage effect at p = 9.5e-66 that was an instrument artifact, an output cap silently truncating the verbose model, caught only by
adversarial review of the completed analysis. Review then found a further confound, each arm resampling its own initial suite; Experiment 2 removes it. Under a pre-registered frozen-shared-round-0 design (five replicates on each of four subjects, seeds committed in advance), same-lineage Critic rounds killed 78% of the survivors the frozen initial suite left standing (mean incremental kill rate 0.783, 95% cluster-bootstrap interval [0.592, 0.935]), a within-replicate causal estimate; the cross-provider configuration showed a positive pilot difference (rate gap 0.178, 95% interval [0.039, 0.347]; magnitude dominated by a single replicate) at 5.5x lower arm cost. This compares two named model-provider-harness configurations, not an isolated lineage effect: part of the gap is one configuration's receipted operational failures, including truncation recurrences, now detected and scored rather than laundered. Cross-model comparisons can inherit the asymmetries of the harness that runs them. We release both protocols, all receipts, and the analysis code.
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Artificial Intelligence · Cross-listed from cs.RO
WCM: World-Cognition Model for Generalizable Human-Robot Interaction
Yuzhen Chen and KC Zhou
Language agents can now interact fluently with users in software, but
robots still struggle to bring comparable interaction to physical tasks. Current
robot-control paradigms, including
vision-
language-action policies and world-model-based planners, are mainly optimized for instruction execution, leaving users with little visibility into why an action is chosen and few mechanisms to redirect, correct, or teach the
robot through interaction. To solve this problem, we present the World-Cognition Model (WCM), a human-centered
embodied agent built on the SLAK architecture (Sensing, Logic, Action, and Knowledge) and an asynchronous runtime. SLAK separates perception,
reasoning, control, and
memory, while the runtime allows
reasoning, dialogue, and execution to proceed concurrently. WCM further introduces a human-in-the-loop teaching mode that enables users to interactively teach the
robot difficult or long-horizon tasks. Teaching episodes and autonomous task rollouts are refined into chain-of-thought supervision to continually improve the model. WCM achieves a 73.8% average success rate across nine real-world human-
robot interaction tasks, including tasks held out from CoT fine-tuning and a long-horizon task learned through teaching.
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Artificial Intelligence · Cross-listed from cs.RO
Real2Sim2Real for Vision-Language-Action Manipulation: An AMD ROCm-Based Pipeline
Qing Yang, Xun Wang, Ziguan Wang et al. (+3 more)
Physical AI – the integration of large
vision-
language-action (VLA) models with
embodied agents that act in the real world – has emerged as the next major frontier for AI, echoed by industry leaders such as Jensen Huang (“the next big thing is Physical AI, AI with a body,” GTC Paris, June 2025) and Dr. Lisa Su (`we're entering the world of Physical AI ... this is where AI enters the real world,' CES 2026). This paper presents an end-to-end, fully AMD-accelerated technology stack for
embodied manipulation, spanning data-center training silicon, Radeon PRO simulation/rendering GPUs, and Ryzen AI edge compute, unified by the open ROCm software stack. We demonstrate that training and deploying VLA-based manipulation policies does not require a CUDA-locked ecosystem. Four progressive demonstrations are presented: (1) a Sim-to-Real manipulation pipeline trained with SmolVLA and deployed on a physical Franka arm; (2) a semantic,
language-grounded object-selection task (`one-of-three'); (3) a Real2Sim synthetic-data generation pipeline that fuses 3D Gaussian Splatting (3DGS) reconstructions of real scenes with the Genesis physics engine; and (4) large-scale reinforcement learning for quadruped and
humanoid locomotion benchmarked across multiple hardware platforms. All pipelines run natively on ROCm + PyTorch on RDNA4 (Radeon AI PRO R9700) and RDNA3.5 (Radeon PRO W7900) hardware and are reproducible on the free Radeon Cloud Platform.
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All authors Qing Yang, Xun Wang, Ziguan Wang, Zhenjiang Li, Hongqiang Wang, Dongdong Weng
Artificial Intelligence · Cross-listed from cs.CL
Beyond Direct Answering: Aligning Educational LLMs as Socratic Guides via Heuristic Reinforcement Learning
Xiaokun Wang, Siyu Song, Wentao Liu et al. (+1 more)
Large
language models (LLMs) deployed in educational settings often behave as direct answerers: they disclose target concepts in the opening turn instead of guiding students through progressive inquiry, as Socratic pedagogy prescribes. We present HeuristicEdu, a two-phase pipeline that aligns Qwen2.5-7B toward Socratic tutoring via supervised warm-up and Group Relative Policy Optimization (GRPO). Training uses SocraticEdu, 797 multi-turn Chinese children's science dialogues reconstructed from a live platform, with a heuristic reward over cognitive depth (R_cog), curiosity engagement (R_eng), and directness (R_dir), together with a K_query correction for student-introduced terms. We introduce Scaffolding Effectiveness (SE) and Conversation Depth (CD) to evaluate outcomes beyond surface fluency. On 30 held-out questions, the best GRPO variant improves SE from 30.0% to 63.3% and lowers keyword leakage from 30.0% to 13.3%. Notably, this best variant omits the directness penalty during optimization, suggesting that explicit anti-leakage terms can conflict with gradient-based behavioral
alignment. An unaligned Qwen-72B baseline reaches 0% SE and 96.7% leakage, showing that scale alone does not induce Socratic behavior.
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All authors Xiaokun Wang, Siyu Song, Wentao Liu, Xiaodong Zou
Artificial Intelligence · Cross-listed from math.CO
An Explicit Counterexample to Stanley's Rankwise Lower-Bound Conjecture for Differential Posets
Xinan Dai, Wenhao Deng, Yingdong Shi et al. (+2 more)
In Problem 6 of his 1988 paper on differential posets, Stanley asked for the least possible cardinality of a fixed rank of an $r$-differential poset and suggested that the minimum should be attained by $Y^r$, the $r$-fold Cartesian power of Young's
lattice. We disprove the resulting universal coefficientwise lower bound. For every $r\geq 3$, we construct an infinite $r$-differential poset $P^{(r)}$ satisfying $\lvert P^{(r)}_4\rvert=\lvert (Y^r)_4\rvert-\lfloor r/3\rfloor$. For $r=3$, the construction replaces thirteen rank-four lower-cover blocks of $Y^3$ by twelve blocks with the same point and pair incidence multiplicities, producing the initial rank sequence $1,3,9,22,50$ instead of $1,3,9,22,51$. A reflection extension then yields an infinite differential poset. The construction does not address the cases $r=1$ and $r=2$.
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All authors Xinan Dai, Wenhao Deng, Yingdong Shi, Tailin Wu, Yuchen Yang
Artificial Intelligence · Cross-listed from cs.LG
Label-free Industrial Fault Detection via Adversarial Inverse Reinforcement Learning: A System for Run-to-Failure Prognostics
Dhiraj Neupane, Mohamed Reda Bouadjenek, Richard Dazeley et al. (+1 more)
Machinery fault detection (MFD) remains heavily reliant on supervised learning, which struggles with the scarcity of fault labels in real-world settings. While reinforcement learning (RL) offers a framework to model the sequential nature of degradation, current “RL-based” MFD methods reduce the problem to a static contextual bandit (CB) formulation: by ignoring state transitions and discarding the temporal discount factor, they collapse to standard supervised classification. We propose an
adversarial inverse reinforcement learning (AIRL) framework that treats MFD as an offline IRL problem. Unlike reconstruction-based approaches that rely on static error margins, or CBs that ignore dynamics, our method recovers an intrinsic "health" reward directly from observational state transitions, requiring neither manual reward engineering nor fault labels. On three run-to-failure
benchmarks (HUMS2023, IMS, XJTU-SY), AIRL is the only method achieving non-saturated post-detection consistency across all
datasets, while CB baselines fail to detect gradual degradation and reconstruction models collapse into always-anomalous states. Code and data: https://github.com/dhirajneupane/AIRL-MFD-DN.
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All authors Dhiraj Neupane, Mohamed Reda Bouadjenek, Richard Dazeley, Sunil Aryal
Artificial Intelligence
ConsistencyGate: Preventing Memory Contamination in LLM Agents via Self-Consistency Admission Control
Yan Zhang and Shibo Li
LLM
agents that operate over many turns accumulate facts in an external
memory store and reuse them as premises for downstream
reasoning. A hallucinated fact written at one step therefore persists as a false premise for every subsequent step, a failure mode we call
memory contamination. Existing
memory management addresses
retrieval and capacity but not write-time correctness; this admission problem cannot be solved by utility- or recency-based criteria, and uncontrolled contamination compounds across long trajectories. We propose ConsistencyGate, a write-time admission gate that, before committing a candidate fact m extracted from context c, queries the LLM K times for a soft support score and admits m only when the average exceeds a threshold. The mechanism is model-agnostic, requires no fine-tuning, and reduces to a single forward pass in a log-probability variant for latency-sensitive deployments. To measure the effect on natural data, we construct two real-conversation
benchmarks (LoCoMo-Contam and MSC-Contam) by planting controlled single-detail corruptions in long-term conversations from LoCoMo and MSC, and complement them with a structured synthetic corpus (MemContam) that isolates a near-oracle upper bound. Across four LLM backbones, ConsistencyGate reduces contamination on every
benchmark relative to a write-everything baseline, with the cost concentrated on facts that are stated only implicitly in the source context. We release all three
benchmarks together with the gate implementation.
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Artificial Intelligence · Cross-listed from cs.CV
HALLELUAI: A Hallucination-Aware AI System for Ultra-Realistic Image-to-Video Generation at Scale
Aniket Sakpal, Yang Jiang, Rouzbeh Davoudi et al. (+2 more)
AI-generated
video is increasingly used across marketing, product storytelling, and creative workflows, yet automated; high-precision quality control remains a major constraint to scaling production. We present HALLELUAI, an end-to-end system that moderates and regenerates image-to-
video outputs to meet expert-level creative standards and deliver ultra-realistic
videos with consistent end-user quality of experience (QoE) at scale. The system integrates a
video moderation module that evaluates frame-level aesthetics, temporal motion fidelity, and fine-grained
hallucination risks relative to the source image, with an agentic regeneration module that iteratively fixes failures through
prompt refinement, controlled camera adjustments, targeted model or image switching, and structured retry strategies. The moderation logic is aligned with domain-specific creative guidelines and produces granular, machine-actionable feedback that directly drives regeneration. In human-in-the-loop evaluations with creative experts, HALLELUAI shows strong
alignment and reliably outputs ultra-realistic, production-grade
videos suitable for product and marketing placements at scale. This framework advances trustworthy AI generated
video content by enforcing visual realism, brand safety, and strict input-image fidelity while enabling image-to-
video generation at scale.
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All authors Aniket Sakpal, Yang Jiang, Rouzbeh Davoudi, Shayan Hassantabar, Mani Najmabadi