Artificial Intelligence · Cross-listed from cs.LG
DIFF-ERO: A Conformance-Aware Loss for Deep Learning in Process Mining
Johannes De Smedt, Jari Peeperkorn, Artem Polyvyanyy et al. (+1 more)
Deep learning has driven many recent advances in process analytics, especially for predictive and prescriptive monitoring. However, standard objectives such as cross-
entropy optimize local next-step likelihoods and only implicitly capture control-flow structure. As a result, models can achieve high
token-level accuracy while permitting imprecise global behaviour. We introduce DIFF-ERO, a conformance-aware loss function for deep learning models on process data. DIFF-ERO is a differentiable formulation of
entropy-based
stochastic conformance that incorporates control-flow information during training. Our approach constructs batch-level
stochastic transition
matrices with soft edge memberships, allowing structural precision and recall signals to directly inform backpropagation. The loss is model-agnostic and can be applied whenever the final representation parametrizes
stochastic transitions. We instantiate DIFF-ERO in
transformer encoder-decoder pipelines for next-activity prediction and use it jointly with cross-
entropy to analyse its theoretical components with respect to convergence. Across
benchmarks comparing other loss functions and targets, DIFF-ERO shows improved predictive performance where structure matters most while maintaining parity elsewhere. At the same time, the learned
stochastic automaton converges towards the structural ground truth, indicating that the network internalizes process model structure.
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All authors Johannes De Smedt, Jari Peeperkorn, Artem Polyvyanyy, Jochen De Weerdt
Artificial Intelligence · Cross-listed from cs.RO
Robust Fall Recovery for Armless Bipedal-Wheeled Robots Via Force-Guided Learning
Haidong Hou, Zhangguo Yu, Tao Han et al. (+6 more)
Fall recovery is critical for autonomous legged locomotion. Existing methods have demonstrated that some legged
robots, such as
humanoids and quadrupeds, are capable of fall recovery from diverse postures by utilizing arms or coordinating multi-legs to generate support forces. Without arms or other legs to provide supportive assistance, a bipedal-wheeled
robot must rely solely on the actuation of its legs, making recovery particularly difficult. To address this, we introduce FTSR (Force-guided Teacher-student framework with Stage-wise Rewards). The force-guided method constructs an external auxiliary force during simulation training that correlates directly with the
robot's real-time height, explicitly formulating this force as an optimizable constraint. Through constrained reinforcement learning, the policy is guided toward reducing force dependency gradually and increasing the body height, developing internal recovery strategies despite having no arms for support. Height-progressive stage-Wise rewards progressively structure posture stabilization during recovery and transition to sustained locomotion, integrated with teacher-student architecture distilling privileged knowledge of force effects and recovery dynamics. After simulation training, the policy is deployed on a physical armless bipedal-wheeled
robot and extensively evaluated. Experiments confirm robust and reliable fall recovery under diverse challenging conditions, demonstrating strong environmental adaptability and motion robustness, while maintaining full post-recovery motion capability. The framework also generalizes effectively to a high-DOF
humanoid, confirming its practical generalizability. The project page is available at https://2350575870.github.io/force-guided.github.io/
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All authors Haidong Hou, Zhangguo Yu, Tao Han, Hengbo Qi, Khaleel Ghazal, Yu Zhang, Yidong Du, Xuechao Chen, Fei Meng
Artificial Intelligence · Cross-listed from cs.IR
ChronoID: Infusing Explicit Temporal Signals into Semantic IDs for Generative Recommendation
Dongdong Nian, Dongqi Fu, Chenliang Xu et al. (+4 more)
Semantic IDs are crucial in generative recommendation, but with a fundamental limitation: temporal information is not well incorporated into semantic IDs. Instead, time influences recommendation only implicitly (e.g., through session construction heuristics, preference
alignment, or sequence order), while existing semantic ID learning remains entirely time-agnostic. This design conflates interactions occurring under distinct temporal contexts into identical semantic representations, implicitly assuming that item semantics and user intent are temporally stationary. Such an assumption is misaligned with real-world recommendation scenarios, where evolving interaction rhythms play a central role. In this work, we investigate where and how the explicit time should be incorporated into semantic ID for generative recommendation. First, we systematically characterize the design space along three orthogonal dimensions of temporal signals and present a unified framework, ChronoID, for time-aware semantic ID learning. Then, by contributing a new time-explicit generation recommendation
benchmark, ChronoID answers the questions: what is the effective way of infusing time, how to design the architecture, and where does the gain come from.
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All authors Dongdong Nian, Dongqi Fu, Chenliang Xu, Yinglong Xia, Hong Li, Hong Yan, Jian Kang
Artificial Intelligence
HarnessX: A Composable, Adaptive, and Evolvable Agent Harness Foundry
Tingyang Chen, Shuo Lu, Kang Zhao et al. (+11 more)
AI
agent performance depends critically on the runtime harness, comprising the
prompts, tools,
memory, and control flow that mediate how a model observes, reasons, and acts. Yet today's harnesses remain largely hand-crafted and static: each new model or task still demands bespoke scaffolding, and the rich traces produced during execution are rarely distilled back into systematic improvement. We introduce HarnessX, a foundry for composable, adaptive, and evolvable
agent harnesses. HarnessX assembles typed harness primitives via a substitution
algebra, adapts them through AEGIS, a trace-driven multi-
agent evolution engine grounded in an operational mirror between symbolic adaptation and reinforcement learning, and closes the harness-model loop by turning trajectories into both harness updates and model training signal. Across five
benchmarks (ALFWorld,
GAIA, WebShop, tau^3-Bench, and SWE-bench Verified), HarnessX yields an average gain of +14.5% (up to +44.0%), with gains largest where baselines are lowest. These results suggest that
agent progress need not come from model scaling alone: composing and evolving runtime interfaces from execution feedback is an actionable and complementary lever. Project homepage: https://darwin-
agent.github.io/HarnessX/.
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All authors Tingyang Chen, Shuo Lu, Kang Zhao, Weicheng Meng, Hanlin Teng, Tianhao Li, Chao Li, Xule Liu, Jian Liang, Zhizhong Zhang, Yuan Xie, Heng Qu, Kun Shao, Jian Luan
Artificial Intelligence
AFFORDANCE20Q: Evaluating Affordance Reasoning from Physical Properties
Yifan Jiang, Meige Yang, Zitong Li et al. (+1 more)
Affordance
reasoning, the inference of an object's action possibilities from its physical properties (e.g., shape and material), is fundamental to human physical understanding and increasingly critical for Large
Language Models (LLMs). However, existing affordance
benchmarks largely expose explicit object identities in the evaluation setup, allowing models to rely on memorized object-affordance mappings rather than
reasoning over physical properties. To address this gap, we introduce Affordance20Q, a novel affordance
reasoning benchmark formulated as a 20-Questions game without exposing the object's identity. In each game, the model identifies a hidden object's affordance from a candidate set by asking yes/no questions about its physical properties. Affordance20Q comprises 1,009 games over 454 objects and 59 affordances, all manually filtered, refined, and annotated. We conduct comprehensive experiments with 15 state-of-the-art LLMs and find a substantial gap (~20 points) compared to human performance. A KL-based information-gain (IG) analysis further shows that models fail to ask discriminating questions as the game progresses. To close the gap, we develop KB-Anchored Rule Induction (KARI), a pipeline based on LLMs that generates affordance rules grounded in evidence from knowledge bases (KBs). KARI improves open-source LLMs by up to 15.2 points, while the limited coverage of KBs hinders further gains. We release all our code and data at https://github.com/1171-jpg/Affordance20Q.git.
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All authors Yifan Jiang, Meige Yang, Zitong Li, Jay Pujara
Artificial Intelligence
SelfEvoSkill: Paired Execution Audits for Skill Revision without External Outcome Supervision
Haowen Gao, Haoran Chen, Can Wang et al. (+5 more)
Agent skills provide LLM
agents with reusable procedures, but a given skill may be incomplete, ineffective, or misleading for the current task. Existing methods for revising skills typically rely on external outcome supervision, such as task rewards, performance on a separate validation set, or feedback from deployment, to decide which updates to retain. However, this supervision may be costly or unavailable when adapting a skill to a new task. Using execution behavior as an alternative signal is challenging: a single run reflects the combined effects of the
agent, task, and workspace, making the skill's contribution difficult to isolate. Comparing executions with and without the skill, while holding these factors fixed, provides evidence about how the skill changes behavior. We introduce SelfEvoSkill, which links these behavioral differences to relevant skill passages and iteratively revises and reevaluates those passages through subsequent executions. Checks derived once from the task's explicit requirements prevent revisions from violating task constraints. Across 89 containerized tasks in 8 professional domains, under the same best-of-two evaluation budget, SelfEvoSkill attains a 73.9% mean task reward, exceeding the no-skill condition by +33.0 percentage points and the
benchmark's curated skill by +17.2 percentage points, while skill
evolution receives neither task rewards nor
benchmark verifier outputs. In controlled ablations, using two with-skill runs instead of a matched with/without-skill pair reduces mean reward by 4.6 percentage points, while limiting revision to one round reduces it by 9.0 points relative to repeated revision. Together, these results show that behavioral differences between matched executions can provide an effective signal for revising skills when outcome supervision is unavailable.
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All authors Haowen Gao, Haoran Chen, Can Wang, Shasha Guo, Liang Pang, Zhaoyang Liu, Huawei Shen, Xueqi Cheng
Artificial Intelligence · Cross-listed from cs.RO
When and How Severely: Scenario-Specific Safety Envelopes for Driving VLAs
Abhinaw Priyadershi and Jelena Frtunikj
Safety certification of
Vision-
Language-Action (VLA) driving planners under ISO 21448 (SOTIF) rests on an Operational Design Domain (ODD) specification that answers two complementary questions: when does the planner start to fail, and how severely does it fail once it does? We evaluate Alpamayo R1, a 10B-parameter open-weight driving VLA, on 15,968 (clip, attack) pairs. We find a conservative-aggregate gap: an aggregate safe threshold of $\sigma \leq 50$ under a 15% average displacement error (ADE) budget masks well-sampled scenarios that tolerate the top of the tested grid ($\sigma = 70$). A Gaussian Mixture Model (GMM) on the changed-explanation subset identifies six discrete severity bands (BIC-optimal $k{=}6$), so two perturbation conditions with the same mean error can differ materially in their share of high-severity (C4/C5) failures. Joining the two analyses on the same corpus surfaces a finding neither yields in isolation: the scenarios with the loosest noise thresholds are not those with the lowest high-severity rate: STOP_SIGNAL concentrates roughly $4\times$ the C4/C5 share of LANE_KEEPING despite tolerating a larger $\sigma$. A deployable SOTIF ODD specification for driving VLAs therefore requires a two-dimensional safety envelope, not a single aggregate value per hazard.
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Artificial Intelligence · Cross-listed from cs.RO
Selective Agentic Recovery for UAV Autonomy with a Persistent Mission Runtime
Taewoo Park, Kyeonghyun Yoo, Seunghyun Yoo et al. (+1 more)
Agentic AI can support unmanned aerial vehicle (
UAV) autonomy by providing high-level recovery
reasoning when local waypoint- or setpoint-based execution encounters blocked passages, repeated no-progress behavior, or mission-level ambiguity. On physical
UAVs, however, remote
reasoning is most useful when it is invoked selectively, since each call introduces latency, resource cost, backend uncertainty, and a need to validate the returned decision. This paper presents Persistent Mission Runtime (PMR), a
UAV recovery framework that keeps the mission loop and safety-critical execution local while using an external agentic reasoner only as an on-demand recovery module. The reasoner selects from predefined recovery skills, and each returned decision is parsed, verified, safety-filtered, and mapped to local executor actions before it can affect flight. PMR introduces learned Cognitive Value of Invocation (learned-CVI), a compact admission gate that estimates when remote agentic
reasoning is likely to improve near-term mission progress enough to justify its operational cost. Across a fixed 400-run Gazebo/PX4
benchmark with eight scenarios, learned-CVI raises hard/ambiguous-regime success from 5.0% under local-only autonomy to 95.0%, outperforms one-shot and periodic
reasoning baselines by 20.0 and 32.5 percentage points, and reduces remote-
agent calls by 16.7% and logged
tokens by 29.2% relative to a manually tuned rule-based invocation baseline.
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All authors Taewoo Park, Kyeonghyun Yoo, Seunghyun Yoo, Hwangnam Kim
Artificial Intelligence · Cross-listed from cs.RO
Universal Manipulation Exoskeleton: Learning Compliant Whole-body Policies with Real-time Torque Feedback
Litian Liang, Jingxi Xu, Xinda Qi et al. (+7 more)
For
robots to work safely in household environments, they need to be compliant and react to torque and force feedback during contact. However, the majority of existing data collection pipelines still lack the ability to capture force and torque data for learning active compliant policies. In this paper, we present Universal Manipulation Exoskeleton (UME), an upper-limb exoskeleton that provides real-time haptic torque feedback while recording whole-arm configurations and joint torque signals for teleoperation. With transparent torque feedback, human operators can even unsheathe kinematically constrained objects while blindfolded. UME is low-cost, lightweight, and portable. Equipped with an embedded IMU, it enables teleoperation for mobile manipulation. With our proposed universal retargeting
algorithm, UME can teleoperate a range of
robots, including the 7DoF OpenArm, 7DoF Franka, and 6DoF X-ARM. We demonstrate that this combination of capabilities enables learning bimanual, whole-body, and active compliant policies that operate effectively in highly constrained spaces. The learned robust autonomous policies achieve high success rates across a variety of tasks, including long-horizon mobile manipulation, force-mediated box flipping, visually occluded box pushing, and space-constrained tabletop manipulation.
Videos, code, and additional information can be found at https://ume-exo.github.io.
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All authors Litian Liang, Jingxi Xu, Xinda Qi, Yujun Cai, Houzhu Ding, Luqi Wang, Zhixin Sun, Jyh-Herng Chow, Ming Yang, Mark Cutkosky
Artificial Intelligence
Closing the Reflection Gap: A Free Calibration Bonus for Agentic RL
Yinglun Zhu
LLMs are increasingly deployed as
agents that interact with external environments and observe feedback such as execution results, error messages, and tool outputs. A well-functioning
agent should be able to leverage this feedback to accurately assess its own performance. Yet we find a persistent reflection gap: LLM
agents tend to mis-assess their own outputs after observing concrete environment feedback – even for questions they correctly answered – and standard RL barely helps due to a credit-assignment mismatch. To close this gap, we propose RefGRPO, a simple yet effective fix that augments standard RL
algorithms with two key ingredients: a free calibration bonus computed by contrasting the
agent's own reflection with the actual outcome (requiring no additional reward model, LLM judge, or external annotation), and a dynamic schedule on its coefficient. Compared to standard RL baselines, our method simultaneously improves reflection calibration (e.g., reduces underconfidence rate $44.4\% \to 7.7\%$) and task accuracy (e.g., $75.1\% \to 76.5\%$) on text-to-SQL across five
benchmarks. The resulting calibrated reflection turns the
agent into its own verifier grounded in environment feedback, which further enables (i) better self-improvement that uses reflections as pseudo-rewards without outcome supervision, and (ii) more effective test-time selective prediction by committing only to rollouts flagged as correct.
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Artificial Intelligence · Cross-listed from cs.CR
From Prompts to Responses: Dual-Sided Data Leakage and Defense in Split Large Language Models
Zixuan Gu, Xiaojun Ye and Yang Liu
Large
language models (LLMs) are increasingly deployed in
privacy-sensitive domains, where users must balance the risk of data exposure through external APIs against the high computational cost of local deployment. Split learning has therefore emerged as a promising paradigm for LLM fine-tuning and inference under limited local resources. However, it introduces new
privacy risks. Prior work primarily studies leakage of private input
prompts, typically via inversion attacks on intermediate representations, while the potential for sensitive information leakage through generative response outputs remains largely unexplored. In this work, we unveil novel vulnerabilities of Split-LLM by presenting Patched Model Inversion with Dual-Sided Initialization (PIDI), a two-stage attack that simultaneously targets both private input
prompts and output responses in Split-LLM settings. It combines dual-sided initialization with a patched inversion strategy to tackle long sequences, substantially outperforming prior inversion methods. To counter threats from both sides, we further propose the Adapter-based DualGuard with Mutual Information Defense (ADMI), which integrates an adapter-based local warmup strategy and mutual information regularization to provide a strong empirical
privacy protection with minimal impact on task performance. Extensive experiments across diverse tasks and models demonstrate that ADMI effectively defends against PIDI and other state-of-the-art inversion attacks. Our code is publicly available at https://github.com/FLAIR-THU/VFLAIR-LLM.
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Artificial Intelligence · Cross-listed from cs.NE
MeEvo: Metacognitive Evolution Combined with Natural Evolution for Automatic Heuristic Design
Zishang Qiu, Xinan Chen, Rong Qu et al. (+1 more)
Large
Language Models (LLMs) have advanced Automatic Heuristic Design (AHD) by enabling heuristic generation through
reasoning and code synthesis. In LLM-based AHD, the LLM reasons about
algorithm design and generates executable heuristic code. Existing architectures adopt two main paradigms: Natural
Evolution applies crossover and mutation to this code to explore diverse strategies, but discards the
reasoning traces behind the design decisions, weakening knowledge retention; Metacognitive
Evolution retains these
reasoning traces and refines them through reflection, but lacks population-level recombination, limiting exploration. These limitations reduce search efficiency, stability, and solution quality on complex problems. To address this gap, we propose MeEvo, an AHD framework that cyclically couples Natural
Evolution and Metacognitive
Evolution with operator balance that shifts from exploration to exploitation. Natural
Evolution explores heuristic code while recording LLM-generated
reasoning traces, fitness values, errors and best heuristic into a shared history; Metacognitive
Evolution then reflects on this history to generate improved heuristics that feed into the next Natural
Evolution cycle. This design enables population-driven exploration and reflection-driven refinement to reinforce each other. Experiments on five optimization problems show that MeEvo achieves stronger performance and lower variance than tested LLM-based AHD architectures, especially on complex constrained tasks.
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All authors Zishang Qiu, Xinan Chen, Rong Qu, Ruibin Bai
Artificial Intelligence
When Should Agent Trust Be Conditional? Characterizing and Attacking Skill-Conditional Reputation in Agent Swarms
Yihan Xia and Taotao Wang
Open platforms increasingly route tasks among heterogeneous LLM
agents–differing in base model, scaffold, and tool stack–whose competence varies sharply by skill: an
agent excellent at one skill may be useless at another. The standard reputation approach summarizes each
agent by a single global trust score, but that scalar is the wrong object here, because routing every task to the globally most-trusted
agent leaves the value of specialization unclaimed. We study skill-conditional trust R(i | k)–the trust to place in
agent i for a task requiring skill k, rather than one score per
agent–and pose three falsifiable questions: when is conditioning worth it, how much cross-skill evidence should be borrowed, and whether that borrowing is safe. A controlled phase-diagram analysis answers the first two: conditional trust wins only in a specific regime–high
agent heterogeneity, sparse per-skill evidence, and correlated skills–and the coupling strength beta that buys this data efficiency is dual-use, because the same cross-skill borrowing is also a laundering channel. On a public
benchmark of 14 genuinely heterogeneous AppWorld
agents, real pools land inside the beneficial regime–a small but genuine gain, with the per-skill best
agent genuinely changing across skills. We then show that an attacker with cheap evidence in one skill and none in a target skill hijacks the conditional router, driving routing regret from 0 to 0.94 on a pool our zero-cost Conditional Information Value Test (CIVT) rates GREEN–while the ungated trust verdict it contaminates reads -0.06 instead of the honest +0.19. A zero-evidence gate bounds the attack but does not eliminate it; we characterize the residual cost under an explicit budget. We do not claim Sybil-resistance–we quantify the trade-off.
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Artificial Intelligence · Cross-listed from cs.CL
OdysSim: Building Foundation Models for Human Behavior Simulation
Xuhui Zhou, Weiwei Sun, Weihua Du et al. (+6 more)
Large
language models are increasingly deployed as human simulators for interactive evaluation and social simulation. Yet helpfulness-driven post-training pulls them toward a homogeneous, overly agreeable assistant register, creating a behavioral Sim2Real gap. We present OdysSim, the largest open systematic investigation of behavioral foundation models, i.e., models trained to simulate human behavior at scale. We propose SOUL, a taxonomy of five capability axes (CONV, SS, COG, ROLE, EVAL) that unifies 62
datasets and 23
benchmark tasks under one framework. Specifically, we curate the OdysSim corpus (21.4M interactions, 10B
tokens, retrofitted with back-generated social contexts), construct the SOUL-Index
benchmark, and develop an end-to-end training recipe combining midtraining, task-specific RL, and expert
distillation. The resulting open 8B OSim model ranks first or tied-first on 8 of 23 tasks, outperforming any individual frontier model by this count, with the strongest gains on conversational and social tasks. Its outputs are also more human-like in length, formatting, and word choice, and it transfers zero-shot to out-of-distribution user simulation on $\tau$-bench, nearly matching real users on reaction
alignment (93.2 vs. 93.5). We further show that LLM-as-judge RL induces reward-hacking patterns, and that our detectors can mitigate them during post-training. Together, our findings suggest that behavioral foundation models require rethinking the LLM training paradigm. We release all artifacts to support future research.
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All authors Xuhui Zhou, Weiwei Sun, Weihua Du, Jiarui Liu, Haojia Sun, Qianou Ma, Tongshuang Wu, Yiming Yang, Maarten Sap
Artificial Intelligence · Cross-listed from cs.RO
Robustness without Wrinkles: Parallel Simulation and Robust MPC for Certified Deformable Manipulation
Wei-Chen Li, Jeffrey Fang, Sasanka Polisetti et al. (+2 more)
We present CORD-SLS, a real-time control method for safe deformable object manipulation, with a focus on ropes and cloth. At its core is a GPU-parallel differentiable simulator with contact smoothing which enables efficient gradient-based planning through intermittent contact. To robustly satisfy constraints under model and sensing uncertainty, we develop a real-time, GPU-parallel output-feedback robust model predictive control (MPC)
algorithm that plans with this simulator. We further show that the simulator accelerates model-based RL for training neural manipulation policies. To improve real-world robustness, we use conformal prediction to calibrate visual-feedback and perception-error bounds for MPC, producing reachable tubes that enable high-probability safe control. We evaluate CORD-SLS on high-dimensional, contact-rich rope and cloth manipulation tasks in simulation and hardware, including obstacle avoidance, routing, folding, and smoothing. Across settings, CORD-SLS achieves millisecond-speed planning, exceeding baselines in safety, speed, and task success.
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All authors Wei-Chen Li, Jeffrey Fang, Sasanka Polisetti, Yuexi Song, Glen Chou
Artificial Intelligence
VeriGeo: Controllable Geometry Question Generation with Numerical and Analytical Verification
Xiaoxian Duan, Zequn Liu and Yingce Xia
Geometry problem generation is useful for AI-assisted education and
multimodal mathematical
reasoning, but reliable synthesis remains difficult because the problem statement, diagram, constraints, and solution should be mutually consistent. Existing methods often trade off controllability and reliability: seed-based rewriting is flexible but weakly verifiable, whereas diagram-first construction improves validity but is less suited to arbitrary user-specified constraints. We introduce VeriGeo, a controllable geometry generation framework grounded in executable
reasoning traces. Given user constraints such as target concepts and difficulty, an Author
agent generates a problem and diagram, and a Solver
agent produces a proof-aligned solution. Both
agents use a shared action sequence that connects natural
language, diagrams,
geometric constraints, and proof steps into a verifiable representation. A three-stage pipeline checks numerical consistency, analytical realizability, and global consistency, using verification-guided reflection to repair recoverable failures and reject unrecoverable ones. Across five LLM backbones, raw generations frequently fail these checks, while VeriGeo repairs a substantial fraction of the invalid attempts. Supervised fine-tuning on 8.7k examples generated by VeriGeo achieves the best reported GeoQA performance among end-to-end
multimodal LLM-based solvers, and obtains strong results on PGPS9K and MathVista-GPS, demonstrating the effectiveness of verified synthetic data for improving
multimodal geometry
reasoning.
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Artificial Intelligence · Cross-listed from cs.LG
Learning Urban Access Costs from Origin-Destination Flows via Inverse Optimal Transport
Paula Joy B. Martinez and Sebastian Felipe R. Bundoc
Cities deliver basic services through mixed public-private facility networks, including schools, clinics, transit providers, and subsidized service points. In these systems, planners often observe where households go, but not the latent cost function through which they trade off factors such as distance, price, and institutional access. We study this urban problem through school choice in the Philippines, where the country's largest national education subsidy is intended to redirect learners from congested public schools to participating private schools. Treating school-to-school enrollment flows as an entropic optimal transport plan, we recover latent choice costs using two complementary inverse optimal transport models: an interpretable distance-banded model with a subsidy term, and a neural cost model trained through a differentiable Sinkhorn forward pass. Applied to 283{,}016 learner trips across 23{,}820 observed flows in the most populated region, the framework estimates a subsidy-equivalent distance, $\lambda^{(k)}$, interpreted as the kilometers of perceived travel cost offset by the subsidy. The case demonstrates how administrative origin-destination data can be transformed into interpretable planning metrics for accessibility-aware subsidy design, facility siting, and urban service allocation.
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Artificial Intelligence · Cross-listed from cs.LG
Learning High Coverage Discriminative Parsimonious Rulesets
Mariamma Antony, Raman Sankaran, Chiranjib Bhattacharyya et al. (+1 more)
Learning systems based on IF-THEN rule representations readily offer interpretability, making them a crucial focus in contemporary AI research. A key objective for such rule sets is to achieve both high discriminative power and interpretability. While existing state-of-the-art
algorithms implicitly prioritize predictive accuracy, they often fall short on one or more quality metrics that ensure interpretability, such as coverage and parsimony of rule sets. Motivated by this, this paper propose the development of CDPR, which aims to create highly accurate and interpretable rule sets for classification problems. To the best of our knowledge, this represents the first attempt to establish such an approach. In this study, we introduce two
algorithms rooted in submodular maximization, which not only provide provable guarantees on coverage but also yield rule sets that are both discriminative and parsimonious. We empirically demonstrate that rule sets learned through our approaches achieve higher accuracy and interpretability and has more than a 2.5-fold improvement in average coverage rates when compared to the next best
algorithm.
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All authors Mariamma Antony, Raman Sankaran, Chiranjib Bhattacharyya, Uma Satya Ranjan
Artificial Intelligence · Cross-listed from cs.CL
Implicit Reasoning for Large Language Model-based Generative Recommendation
Yinhan He, Liam Collins, Bhuvesh Kumar et al. (+3 more)
Large
Language Models (LLMs) are increasingly adopted as backbones for Generative Recommendation (GR), promising access to pretrained world knowledge. Yet reliably invoking this knowledge for GR remains poorly understood. A key obstacle is that LLM-based GR typically represents items with Semantic IDs (SIDs), disrupting LLMs' natural-
language reasoning interface because these
tokens are unseen by the LLM during pretraining. Existing approaches address this with expensive multi-stage pipelines that ground SIDs and elicit explicit rationales, but offer limited insight into when and why each stage is necessary. In this work, we systematically decompose explicit
reasoning training pipelines for LLM-based GR, revealing three key limitations: weakened world-knowledge verbalization, misalignment between SID and natural-
language token embedding spaces, and sensitivity to rationale quality, all of which hurt explicit
reasoning performance. To circumvent these issues, we propose PauseRec, a lightweight implicit
reasoning paradigm tailored for GR. PauseRec is exceptionally practical, avoiding costly
reasoning trace acquisition and
reasoning alignment training, leading to a multitude of benefits: (1) it outperforms standard explicit CoT methods by up to 6.22%, (2) it reduces training cost by up to 65% GPU hours, and (3) it speeds up inference by up to 71.3%. These results position PauseRec as a lightweight alternative to explicit rationale generation, enabling more effective and efficient LLM-based GR.
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All authors Yinhan He, Liam Collins, Bhuvesh Kumar, Jundong Li, Neil Shah, Donald Loveland
Artificial Intelligence · Cross-listed from cs.SD
Spatio-Temporal Audio Language Modeling for Dynamic Sound Sources
Oh Hyun-Bin, Kazuki Shimada, Yuhta Takida et al. (+6 more)
Sound events have semantic identities, locations, and trajectories, yet existing audio-
language models typically encode clips as global event content. Conversely, sound event localization models track source directions over time but offer limited semantic coverage for
language reasoning. To bridge this gap, we introduce ST-AudioQA, a spatio-temporal audio QA
dataset and
benchmark built from first-order ambisonic (FOA) renderings of static and moving sound sources. Each scene provides source identity, activity, direction, distance, and motion metadata, enabling dense trajectory supervision and QA about what is sounding, where it is, how it moves, and how sources relate. We further introduce ST-Audio Encoder, which jointly learns event semantics and time-resolved source trajectories, and ST-AudioLM, which exposes these audio
tokens to an LLM for spatio-temporal audio QA. Experiments show that this representation improves the balance between semantic preservation and localization and yields stronger
reasoning than static spatial and localization-oriented baselines. Beyond the controlled
benchmark, ST-AudioLM shows zero-shot transfer to real recordings, with further gains after light adaptation. Project page: https://st-audiolm.github.io/.
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All authors Oh Hyun-Bin, Kazuki Shimada, Yuhta Takida, Kim Sung-Bin, Toshimitsu Uesaka, Takashi Shibuya, Kyeongyoon Lee, Tae-Hyun Oh, Yuki Mitsufuji
Artificial Intelligence · Cross-listed from cs.CV
Conditioning Matters: Stabilizing Inversion and Attention in Diffusion Image Editing
Zheyuan Zhan, Hongchen Li, Can Wang et al. (+6 more)
Inversion-based image editing offers flexible and training-free control but still struggles with inversion accuracy and the trade-off between editing fidelity and background preservation. While recent methods improve inversion formulations or
attention interactions, the role of textual conditioning in shaping
diffusion dynamics and editing behavior remains underexplored. We show both empirically and theoretically that the precision of textual conditioning influences inversion stability by modulating the geometry of the
diffusion velocity field, while also affecting the consistency of cross-branch
attention during editing. These effects directly impact background preservation and semantic fidelity. Building on this analysis, we propose SimEdit, a conditioning-aware framework with two complementary components: (a) conditioning refinement, which constructs conditioning signals with improved semantic precision and structural
alignment to facilitate stable inversion and consistent
attention manipulation, and (b)
token-wise cross-branch
attention control, which separates edit-relevant and structure-preserving components and modulates them asymmetrically during
attention manipulation. Extensive experiments on PIE-Bench demonstrate that SimEdit consistently improves both inversion reconstruction quality and editing performance over previous
attention-manipulation approaches. Our code is available at https://github.com/zju-pi/SimEdit.
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All authors Zheyuan Zhan, Hongchen Li, Can Wang, Yinfei Ma, Mingzhen Huang, Ruoshi Bai, Jiawei Chen, Siwei Lyu, Defang Chen
Artificial Intelligence · Cross-listed from cs.LG
Recovering Stranded Discrimination in Knowledge Tracing: Per-Item Bias Correction via Empirical-Bayes Shrinkage
Xiaoran Yan, Cheng Tang and Atsushi Shimada
Deployed knowledge-tracing models are typically frozen after training, yet systematic per-item logit bias arises, from limited per-item expressivity in backbone architectures and from post-deployment shifts in item properties, degrading prediction quality. Global post-hoc calibrators such as Platt scaling, temperature scaling, and isotonic regression improve probability estimates but leave discriminative ability, as measured by AUC, unchanged. This AUC invariance is a structural consequence of monotone score-only transforms; recovering the stranded discrimination requires conditioning on item identity. We propose SLC (State-space Logit Correction), which converts binary observations to Gaussian pseudo-observations via Laplace/IRLS, applies empirical-Bayes shrinkage through a Kalman smoother, and fits an offset-Platt link. The state-space formulation also yields a detectability bound that characterizes the Bernoulli information floor, explaining why temporal tracking provides no benefit at current data densities. Across four
datasets, five backbones, and three seeds, SLC improves AUC on all four
datasets and NLL on three, with the advantage concentrating on sparse items. Cross-domain controls suggest that the same phenomenon can arise beyond education when the deployed backbone leaves entity-level bias.
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Artificial Intelligence · Cross-listed from eess.SP
FAConformer: Frequency-Aware Convolutional Transformer for Auditory Attention Decoding
Ziwei Wang, Xingyi He, Tianwang Jia et al. (+2 more)
Auditory
attention decoding (AAD) aims to infer the attended speaker from neural responses in multi-speaker acoustic environments and is a key problem for neuro-steered hearing systems. Although recent studies have achieved encouraging progress, existing AAD models still do not fully exploit frequency domain electroencephalography (
EEG) information. In particular, most approaches introduce multi-band information through handcrafted feature extraction or direct cross-band feature concatenation, which mainly exploit frequency information at a shallow level and may overlook band-specific patterns and cross-band interactions. To address these limitations, this paper proposes FAConformer, a frequency-aware CNN-
Transformer framework for AAD that explicitly integrates band-specific encoding and adaptive cross-band interaction. Specifically, FAConformer first decomposes
EEG signals into multiple frequency bands and assigns each band to an independent CNN-
Transformer encoder for band-specific modeling. The resulting band-wise features are then adaptively fused by a carefully designed frequency-aware
attention (FAA) module that models cross-band dependencies by treating band-wise features as
tokens. Further, band-wise auxiliary supervision (BAS) is introduced to prevent weakly contributing branches from being under-optimized during joint training. In this way, FAConformer performs frequency-aware modeling that more effectively exploits frequency domain information. Extensive experiments on two public AAD
datasets with three decision-window lengths demonstrated that FAConformer consistently outperformed 12 competitive baselines, surpassing the current state-of-the-art model by 4.9%. Further analyses of band importance, ablation, and parameter sensitivity verify the effectiveness, robustness, and interpretability of the proposed framework. Code is available at https://github.com/wzwvv/FAConformer.
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All authors Ziwei Wang, Xingyi He, Tianwang Jia, Hongbin Wang, Dongrui Wu
Artificial Intelligence
FactoryLLM: A Safe and Open-Source AI Playground for Evaluating LLMs in Smart Factories
Yash Pulse, Yong-Bin Kang, Abhik Banerjee et al. (+2 more)
Fault diagnostics and recovery in smart factories is challenging because critical information is dispersed across manuals of multiple machines which are interconnected through the manufacturing process. Large
Language Models (LLMs) can provide a promising approach. In this paper, we propose FactoryLLM, a safe and open-source AI playground designed for evaluating different LLM-based
retrieval-augmented generation (RAG) models by analysing documents from multiple machines across the manufacturing process. FactoryLLM enables the user to configure the LLM, and assess performance when
reasoning over multiple documents, through a dual evaluation setup using both RAGAS and NVIDIA's LLM-as-a-Judge metrics. FactoryLLM is safe because it allows users to run local or open-source LLMs without sharing sensitive industrial data, providing a controlled environment for experimentation. We demonstrate the efficacy of FactoryLLM through a case study which involves an Autonomous Intelligent Vehicle and its Mobile Planner software, evaluating three LLMs across 30 maintenance queries derived from approximately 600 pages of cross-machine documentation. The results suggest that FactoryLLM is effective in cross-machine document
reasoning: every model achieved a groundedness score above 0.88. The full code and documentation for community to test FactoryLLM with their manufacturing specific scenarios are publicly available.
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All authors Yash Pulse, Yong-Bin Kang, Abhik Banerjee, Abdur Forkan, Prem Prakash Jayaraman
Artificial Intelligence · Cross-listed from stat.ME
A Two-Stage Statistical Framework for Evaluating Associative Interference in Large Language Models
Achraf Cohen and Andrew Kincaid
Large
language models (LLMs) are increasingly evaluated for bias using adaptations of human psychological paradigms, yet methodological limitations-particularly the conflation of refusal behavior with task performance-have hindered clear interpretation. Here, we adapt the Implicit Association Test (IAT) to a controlled, forced-choice framework and introduce a two-stage modeling approach that separates response compliance from task-consistent classification. Across three contemporary LLMs (Claude Sonnet-4, Gemini 2.5 Pro, and GPT-5), we evaluate associative interference, defined as reduced task-consistency in incongruent relative to congruent conditions. While compliance with the structured response format was uniformly high, interference effects varied substantially across models and domains. Claude Sonnet-4 exhibited strong interference in the Gender–Career domain (DeltaP = 0.086, 95% CrI [0.026, 0.173]) and smaller but credible effects in Gender–Science. Gemini 2.5 Pro showed attenuated interference, and GPT-5 exhibited minimal or no detectable interference across domains. These findings demonstrate that IAT-style associative asymmetries are not a universal property of LLMs, but instead depend on model-specific characteristics. By isolating interference from compliance and modeling item-level variability, this study provides a principled framework for evaluating structured response patterns in LLMs. The results highlight the importance of model-specific assessment and suggest that associative interference can be substantially mitigated in modern systems.
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Artificial Intelligence · Cross-listed from cs.LG
Numbers Already Carry Their Own Embeddings
Suhyun Bae and Donghun Lee
We introduce Adelic operation-preserved
embeddings (AOE), a training-free representation that captures both a number's real value and its modular (p-adic) signatures. This construction preserves additive and multiplicative structure by design, turning numerical input into
embeddings that "speak in the
language of mathematics." Unlike prior approaches that rely on task-specific retraining, AOE is plug-and-play and drops seamlessly into existing architectures. On algebraic combinatorics
benchmarks, it delivers consistent gains including the first-ever perfect accuracy on the Weaving Pattern task-while suggesting a principled path forward for overcoming the long-standing "number problem" in AI.
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Artificial Intelligence · Cross-listed from cs.CV
FEMOT: Multi-Object Tracking using Frame and Event Cameras
Shiao Wang, Xiao Wang, Chao Wang et al. (+6 more)
Conventional RGB cameras have been widely used in multi-object tracking due to their ability to capture rich appearance and semantic information. However, their performance is often degraded under complex real-world challenges, such as motion blur, low illumination, and overexposure. Bio-inspired event cameras offer high temporal resolution and high dynamic range, providing complementary cues under extreme scenarios. Nevertheless, RGB-event multi-object tracking remains underexplored due to the lack of large-scale and well-annotated
datasets. To address this issue, we propose FEMOT, a large-scale RGB-event multi-object tracking
dataset that covers diverse real-world scenarios and 14 challenging attributes. With both RGB and event data as well as high-quality annotations, FEMOT provides a reliable platform for systematically evaluating RGB-event multi-object tracking methods. Based on FEMOT, we retrain and evaluate over ten strong trackers, thereby establishing a comprehensive
benchmark for future research. Furthermore, we propose FEMOTR, a
multimodal tracking framework that decouples RGB and event features and fuses them in the frequency domain, thereby effectively exploiting their complementary characteristics for robust object localization and identity association. Extensive experiments on FEMOT and DSEC-MOT
datasets demonstrate the effectiveness of the proposed method. The source code and
benchmark dataset have been released on https://github.com/Event-AHU/FEMOT.
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All authors Shiao Wang, Xiao Wang, Chao Wang, Yitao Li, Menghao Liu, Bo Jiang, Yaowei Wang, Yonghong Tian, Jin Tang
Artificial Intelligence · Cross-listed from cs.CV
Clay-CNN Hybrids: Leveraging Geospatial Foundation Models as Auxiliary Context for Landslide Detection
Huong Binh Vu
Rapid post-event landslide mapping is essential for disaster response but remains difficult to automate due to extreme class imbalance. This study evaluates whether Clay v1.5, a Geospatial Foundation Model (GFM), can improve pixel-level landslide
segmentation on the Landslide4Sense (L4S)
benchmark, which contains 3,799 training chips with 14 Sentinel-2 and terrain bands and approximately 2% positive pixels. We compare three strategies: Clay as the primary encoder with multi-scale residual terrain fusion, a U-Net backbone augmented with Clay semantic context at the bottleneck, and a standard U-Net baseline. The hybrid U-Net + Clay model with two-stage Low-Rank Adaptation (LoRA) achieved the best test F1 of 64.5 +/- 1.8% over three seeds, surpassing the Clay-only backbone (55.2 +/- 3.6%) and the U-Net baseline (59.9%). Clay as a standalone encoder underperformed the U-Net due to the absence of multi-scale skip connections, but its pretrained representations consistently improved performance when injected as auxiliary context. These findings suggest that GFMs are most effective for landslide detection when they complement spatially detailed convolutional architectures rather than replace them.
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Artificial Intelligence · Cross-listed from cs.LG
Rethinking Backdoor Adversarial Unlearning through the Lens of Catastrophic Forgetting in Continual Learning
Zhenqian Zhu, Yamin Hu, Yujiang Liu et al. (+5 more)
Existing studies reveal that current
backdoor defenses exhibit limited robustness and often fail against specific types of attacks. More concerningly, prevailing safety tuning strategies tend to provide only superficial safety protection, as they fall short of completely eliminating the
backdoor effects. In this work, we present a novel formulation of
backdoor learning and unlearning as a sequential, three-stage process from a continual learning perspective. Within this framework, we formally define complete
backdoor unlearning and further derive the necessary conditions for achieving it based on the mechanism of catastrophic forgetting. Guided by these insights, we propose Blind Inversion-
Backdoor Adversarial Unlearning (BI-BAU), which formulates the generation of
adversarial examples satisfying the unlearning conditions as a blind inversion problem. We solve this by integrating the bi-level optimization process of
adversarial training into an Expectation-Maximization (EM)
algorithm framework to optimize the maximum a posteriori (MAP) objective. Furthermore, BI-BAU is extended to untargeted
adversarial scenarios with unknown target classes, as well as to multi-modal contrastive learning tasks, enhancing its applicability to real-world deployment scenarios where pre-trained models may be compromised. Extensive experiments demonstrate that our method exhibits general applicability across a wide spectrum of
backdoor attacks and can effectively and thoroughly eliminate the
backdoor effects from a
backdoor model.
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All authors Zhenqian Zhu, Yamin Hu, Yujiang Liu, Luping Wei, Wenbo Hou, Bin Li, Haodong Li, Wenjian Luo
Artificial Intelligence · Cross-listed from cs.IR
Knowledge Graph Enhanced Memory-Augmented Retrieval for Long Context Modeling
Ghadir Alselwi, Basem Suleiman, Hao Xue et al. (+4 more)
Long-context
language modeling requires not only extending context windows but maintaining coherent understanding of entity states and relationships across thousands of
tokens – a challenge that semantic similarity alone cannot address. KGERMAR addresses this by constructing dynamic, context-specific knowledge graphs from input text during inference, enabling domain-adaptive
retrieval that leverages both semantic similarity and explicit entity relationships. The framework performs real-time entity and relation extraction to build contextual knowledge graphs, then integrates graph-structural
embeddings with textual semantics through a multi-component
memory architecture. Three
memory banks – contextual, semantic, and structural – are maintained with
retrieval signals fused via learned weights to capture both surface-level semantics and deeper relational patterns. Evaluated on SlimPajama (84.7K training examples), WikiText-103 (4,358 examples), PG-19 (100 examples), and Proof-pile (46.3K examples), KGERMAR achieves up to 8.5% lower perplexity and 2–2.5x better
memory efficiency than
memory-augmented baselines across context lengths from 1K to 32K
tokens, with superior in-context learning performance across five NLU tasks. The dynamic knowledge graph construction approach advances
memory-augmented
language modeling by enabling domain-specific knowledge representation that adapts to input contexts rather than relying on fixed knowledge bases.
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All authors Ghadir Alselwi, Basem Suleiman, Hao Xue, Shoaib Jameel, Hakim Hacid, Flora D. Salim, Imran Razzak