Artificial Intelligence
Confidence-Gated Transductive Test Generation for Code Reranking
Sungjae Lee, Youngsik Yoon, Seockbean Song et al. (+3 more)
Test case synthesis is crucial for evaluating and ranking programs generated by large
language models (LLMs). However, constructing high-quality test cases remains challenging because reliable expected outputs are often difficult to obtain. We propose Confidence-Gated Transductive Test Generation (CoTT), which first uses an efficient inductive procedure and invokes transductive generation only when inductive confidence is low. This adaptive design improves output reliability while allocating extra computation only when needed. On code reranking
benchmarks, CoTT outperforms prior baselines across the reported metrics while reducing cost relative to applying transductive generation to every input. These results show that confidence-based allocation of test-time computation provides a favorable efficiency-effectiveness trade-off with a single efficient LLM.
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All authors Sungjae Lee, Youngsik Yoon, Seockbean Song, Siwei Wang, Wei Chen, Jungseul Ok
Artificial Intelligence · Cross-listed from eess.SY
Adaptive Agent Design
Raj Kiriti Velicheti, Subhonmesh Bose and Tamer Başar
We consider an
agent acting against a general non-Markovian environment. The
agent maintains its
agent states, but is free to choose a transition kernel across those states and optimize its state-feedback control policies. We study the bi-level
agent design problem that optimizes the transition kernel and the policy it induces, given said kernel with offline data of observations and actions obtained via a behavioral policy. For general environments, we show that a soft $Q$-learning
algorithm converges almost surely to the fixed point of a soft Bellman equation defined by the stationary averages that the behavioral policy and the chosen kernel induce, and we delineate what separates the resulting policy from an optimal one. In partially observed Markov decision problems, we analyze convergence properties of parametrized transition kernel design via zero-th order and
Bayesian optimization techniques.
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Artificial Intelligence
When Does AI Augment Work? A Workflow-Level Framework for Human-Agent Collaboration
CIVIC-AI Collaboration: Jiaying Wu, Caleb Ziems, Raymond Chan et al. (+18 more)
We aim to characterise the value of artificial intelligence in the workplace. Current studies largely measure this value in terms of the current automation capabilities and public adoption of AI. However, such metrics ignore the greater impacts of human–
agent collaboration in transforming the nature of work. To account for this, we must expand the scope of our analysis beyond atomised tasks of today, and instead focus on how AI can augment entire workflows of the future. To ground this analysis, we establish a precise definition of AI augmentation comprising six conditions, spanning durable net value, meaningful human control, accountability and recovery, and long-term human development through learning, career pathways, and job purpose. We elaborate on these conditions and apply the framework in a case study of AI-mediated social surveys. We conclude by outlining how organisations, researchers, and government leaders can use this framework to make sense of the future of work.
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All authors CIVIC-AI Collaboration: Jiaying Wu, Caleb Ziems, Raymond Chan, Nancy F. Chen, Corlyss Chua, Gerard Chung, Jungpil Hahn, Wee Sun Lee, Zhengyuan Liu, Jamie Ng, Desmond C. Ong, Jeryl Ong, Da Ren Soon, Tianqi Song, Zhi-Xuan Tan, Sixing Tao, Emily Yang, Yajing Yang, Stella Xin Yin, Min-Yen Kan, Diyi Yang
Artificial Intelligence
TripPattern: A Pattern-based Text Watermarking Method for Large Language Models
Sangjun Moon, Dasom Choi, Jingun Kwon et al. (+3 more)
Text watermarking techniques have gained significant
attention for identifying machine-generated text and mitigating risks from large
language models (LLMs). Existing methods typically divide an LLM's vocabulary into green and red
tokens, but encouraging generation toward green
tokens can reduce text quality and naturalness. To address this, we propose TripPattern, a watermarking framework that formulates text watermarking as a pattern-based matching task using three vocabulary partitions. TripPattern divides the vocabulary into one neutral group and two pattern groups. During generation, the model alternates
token selection between the two pattern groups to embed detectable patterns, while neutral
tokens are selected independently to improve flexibility and preserve naturalness. For detection, TripPattern uses pattern-based statistical tests that provide interpretable p-values by measuring how often adjacent
tokens alternate between the pattern groups. Theoretical analysis and empirical evaluations on four
multilingual datasets show that TripPattern maintains LLM generation quality while achieving robust watermark detectability.
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All authors Sangjun Moon, Dasom Choi, Jingun Kwon, Hidetaka Kamigaito, Taro Watanabe, Manabu Okumura
Artificial Intelligence · Cross-listed from cs.CL
Not All Speech Is Intent: Adaptive Self-Correcting Inference Layer for Post-ASR False Wake-Up
Preeti Saraswat, Divya Neelagiri and Anil Yadav
False wake-up activations remain a persistent challenge in conversational AI.
Speech phonetically similar to a device's wake word can produce a syntactically valid and semantically coherent ASR transcript that the assistant incorrectly executes. Most existing systems make a single intent decision in isolation, without a mechanism to learn from recurring errors over time or adapt to individual users through personalized learning. We introduce the Feedback-Driven Adaptive Self-Correcting Inference Layer (ASCIL), a complementary post-ASR correction framework that re-evaluates wake-up intent before response generation by fusing acoustic
embeddings, linguistic cues, device context, and patterns from past misclassifications. ASCIL interprets implicit signals, including hesitation, disengagement, and silence, and explicit signals, including cancellation and repetition, as automatically inferred, noisy behavioral indicators of potential misclassification. These signals drive online pattern updates without manual annotation, whereas the intentional/unintentional reference labels used for offline evaluation are human-annotated. It generalizes from prior errors, applies corrective adjustments at inference time, and continuously updates in parallel with natural-
language execution. Evaluated on a proprietary
dataset of 3,667 interactions with human-annotated intentional/unintentional reference labels spanning 14 acoustic and contextual conditions, ASCIL achieves 54.27% relative error reduction on a session-disjoint subset constructed from baseline failures, and up to 24.39% relative error reduction at threshold 0.90 on the issue-tagged evaluation slice. These gains are achieved while improving intentional acceptance rates, with a median added latency below 60 ms in the reported
benchmark.
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Artificial Intelligence
Beyond Vector Similarity: Hierarchical Context-Aware Graph RAG vs Standard RAG in Enterprise Code Migration
Nilesh Jaiswal, Aniket Agrawal, Arjit Shukla et al. (+4 more)
As enterprises modernize legacy monolithic systems to microservices, Large
Language Models (LLMs) are heavily utilized for automated code translation. However, traditional vector-based
Retrieval-Augmented Generation (Standard RAG) struggles to capture topological relationships. It fetches isolated chunks that sever inheritance chains, leading to high compilation failure rates. This paper introduces a Hierarchical Context-Resident Graph (HCRG) methodology to resolve these limitations. Our pipeline uses tree-sitter for Abstract Syntax Tree (AST) extraction, maps architectural edges into a Google Cloud Spanner Property Graph, and serializes this structure into a Gemini Context Cache for topological, parent-first code translation. We shift evaluation from naive text-overlap to a custom 7-metric Software Engineering framework. Traditional metrics like CodeBLEU (which scored 91% for both methods) effectively masked Standard RAG's structural failures behind syntactically plausible but broken code. Empirically, Graph RAG decisively mitigates dependency loss: API
hallucination rates dropped from 56.4% to 16.2%, Dependency Resolution Quality improved from 34.8% to 65.9%, and Parent-Child Consistency rose from 26.7% to 45.5%. However, Graph RAG introduces specific trade-offs. The dense global context causes defensive over-engineering by the LLM, reducing Cyclomatic Complexity Consistency from 71.6% to 46.7%, and slightly degrades Docstring Preservation (67.0% to 61.0%). Ultimately, while trading code complexity for reduced
hallucinations, Graph RAG provides a substantially more viable, architecturally sound path for automated enterprise codebase modernization.
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All authors Nilesh Jaiswal, Aniket Agrawal, Arjit Shukla, Divya Malhotra, Saurabh Garg, Suchit Puri, Suddhasatwa Bhaumik
Artificial Intelligence · Cross-listed from math.OC
Linear Exponential Quadratic Gaussian Covariance Steering
Chiran B. Cherian, Yasemin Isik and Abhishek Halder
We formulate and analyze the linear exponential quadratic Gaussian (LEQG) covariance steering problem in continuous time over a given deadline (finite time horizon). The solution for this problem can be seen as a risk-sensitive Schrödinger bridge between Gaussian endpoints in the linear quadratic setting. Unlike the risk-neutral case, the LEQG covariance steering controller–still a linear state feedback–can no longer be written in closed form. We show that the optimal controller is parameterized by a symmetric
matrix solving an algebraic equation that encodes the implicit dependence on the risk-sensitivity parameter. We explain how the structure of this optimal controller significantly generalizes the existing results for the risk-neutral case. Building on these results, for the matched noise and input channel case, we prove the existence-uniqueness of solution for the LEQG covariance steering problem in the neighborhood of the known risk-neutral optimal solution. We give an illustrative numerical example.
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Artificial Intelligence
EvoRS: On-Policy Self-Evolution of Reward Systems for Open-Ended Reinforcement Learning
Weiyuan Li, Aili Chen, Xintao Wang et al. (+11 more)
Open-ended reinforcement learning often relies on rubric-based rewards for tasks without directly verifiable answers. Yet the policy and reward system form a dynamic feedback loop: as the policy optimizes the current reward, an initially useful reward system may become unreliable due to reward hacking or reduced response discriminability. The reward system should therefore evolve rather than remain fixed during training. Existing dynamic-rubric methods adapt evaluation criteria, but reward failures can also arise from scoring mechanisms or signal composition. We introduce EvoRS, a self-evolving RL framework that evolves the reward system from on-policy experience, representing it as an executable Reward-DAG. Specifically, an agentic designer updates this system from on-policy rollouts and reward traces to maintain train-time reliability. Across writing and roleplay, EvoRS achieves the best quality under all three judges, outperforming the policy by \(2.107\) and \(4.767\) points, respectively, while reducing reward hacking and coverage failures and preserving reward informativeness. Ablations confirm that a comprehensive fixed reward system cannot remain reliable in open-ended tasks and must evolve throughout training.
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All authors Weiyuan Li, Aili Chen, Xintao Wang, Yikai Zhang, Qingqing Dong, Jinghan Xu, Hongru Hou, Wenxuan Zhao, Chengkun Lang, Jun Gao, Yuanli Guo, Hongcheng Guo, Yanghua Xiao, Deqing Yang
Artificial Intelligence · Cross-listed from cs.CV
Bridging Vision Foundation Model Priors with CLIP for Spatial-aware Few-shot Anomaly Detection in Medical Images
Juzheng Miao, Yuchen Yuan, Cheng Chen et al. (+1 more)
Vision-
Language Models such as CLIP enable effective few-shot medical anomaly detection (AD) via strong image-text semantic
alignment. However, their globally contrastive pretraining lacks explicit spatial supervision, limiting precise lesion localization. In contrast,
Vision Foundation Models (VFMs) such as DINO learn spatially coherent patch representations via self-
distillation and local-to-global consistency, better capturing fine-grained anatomical structures. Leveraging this complementarity, we propose Spatial-FAD, a spatial-aware few-shot medical AD framework that improves lesion localization by combining VFM spatial priors with CLIP semantics. Specifically, we introduce a VFM-enhanced adapter that injects a structural affinity prior derived from DINO into CLIP features. This structure-guided refinement encourages visual
embeddings to better adhere to lesion boundaries while maintaining semantic
alignment. To address the loss of spatial detail from patchification and the limited input resolution of CLIP, we adopt a sliding-window aggregation strategy. This generates high-resolution, spatially dense
embeddings to further enhance localization granularity. Moreover, we introduce a prototype-enhanced support
memory scheme to efficiently exploit the few-shot support set. This module stores compact prototypes for normal and abnormal patterns, reducing
memory costs while boosting performance by fusing patch-to-prototype and image-text similarities. Extensive experiments on three
benchmark datasets, including Liver CT, Retinal OCT, and
Brain MRI, demonstrate that Spatial-FAD significantly outperforms state-of-the-art methods, especially in lesion
segmentation. Notably, in the 4-shot scenario, our method achieves an average improvement of over 11.4% in Dice score and 1.8% in AUC. Code is available at: https://github.com/JuzhengMiao/Spatial-FAD.
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All authors Juzheng Miao, Yuchen Yuan, Cheng Chen, Pheng-Ann Heng
Artificial Intelligence
Do LLMs Trust the Accuser or the Accusation? Measuring Belief Shifts in Werewolf
Yu-Yu Yang, Ti-Rong Wu, Hung Guei et al. (+2 more)
Social-deduction games such as Werewolf are increasingly used to evaluate LLM
agents, but existing evaluations often rely on final game outcomes. We propose a belief-shift evaluation
benchmark in Werewolf for analyzing communication skills through belief updating. Using LLM-played games, we annotate suspicion and accusation messages and measure how an observing village-side model's beliefs change after each message. We evaluate 40 open-weight LLM configurations on 1,224 annotated messages. Our results show that larger models better distinguish true wolves from villagers based on game history, but accusations still strongly influence their beliefs. Models become more suspicious of the accused target and less suspicious of the accuser, especially when the accuser is trusted, even if the accuser is wolf-aligned. Larger models better resist accusations from accusers they already distrust. Overall, our findings suggest that current open-weight LLMs up to 120B parameters still struggle to integrate accusation content with source trust in strategic communication. Our
benchmark and code are available at https://rlg.iis.sinica.edu.tw/papers/werewolf-accusation-
benchmark.
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All authors Yu-Yu Yang, Ti-Rong Wu, Hung Guei, Hsing-Yu Chen, I-Chen Wu
Artificial Intelligence · Cross-listed from cs.LG
3D Digital Twin Visualization of Multiclass GRF-Based Gait Disorder Classification
Nayoung Son and Minwoo Shin
Automated gait analysis requires accurate classification and interpretable outputs. We propose an integrated framework for classifying healthy gait and multiple musculoskeletal impairment groups using bilateral ground reaction force (GRF) and center-of-pressure (COP) signals. The signals were normalized over the stance phase and standardized using training-set statistics. The model achieved a validation accuracy of 99.00% and a test accuracy of 90.07% under a session-level split. Class-specific $\epsilon$-LRP identified positive and negative contributions across both sides, multiple signal components, and different stance phases. Separately, the processed GRF signals and model predictions were synchronized within a Blender-based 3D visualization, enabling sample-level inspection of gait trials and classification results. The proposed framework integrates classification, explainability, and 3D visualization to improve model transparency. The source code is available in the following repository: https://github.com/nyoico/grf-gait-3d-visualization.git
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Artificial Intelligence · Cross-listed from cs.RO
IMPLY: Physically Anchored Consistency for World-Model Rollouts
Aman Mehta and Riya Baviskar
A world model asked what happens if an object is pushed at several speeds produces several futures. If the model has the object in mind, those futures agree about it: each implies the same mass and friction. The consistency checks now used to vet world-action models ask whether a model's futures agree with each other, and none of them knows any physics. We show that this is not enough, and what to do instead. IMPLY reads the physics each rollout implies by inverting a simulator and scores a set of rollouts by how well one object explains all of them, anchored to two calibration pushes the model has observed. In a controlled setting, self-consistency gives a perfect score to a model that ignores the object and always predicts a typical push; anchoring exposes it (AUROC 0.70 versus 1.00). On a real model, V-JEPA 2-AC adapted to the scene, the same thing happens. Given its own calibration pushes the model tracks the object (per-object correlation with the truth 0.91); given another object's, it does not (0.05). Self-consistency cannot tell these apart, preferring the right evidence on 52% of objects, chance level, while anchored disagreement prefers it on 73% and correlates 0.92-0.99 with the rollouts' error. Used to choose among candidate rollout sets, it comes within 0.003 of an oracle that sees the truth. A model that has internalised the wrong object is exactly as self-consistent as one that has internalised the right one; consistency has to be anchored to evidence.
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Artificial Intelligence · Cross-listed from cs.DL
Debiasing as a Measurement Intervention: Calibrated Ties and Resolution Loss in LLM-as-a-Judge Evaluation
Liang Zhao, Yong Wang and Jiangzhe Chen
LLM-as-a-judge protocols are commonly debiased by instructing judges to ignore presentation cues such as citation formatting, source labels, and evidence-display style. We show that this intervention can suppress bias while damaging the resolution of the measurement instrument. We introduce TraceJudgeBench, a diagnostic
benchmark for
auditing citation-like artifacts in RAG and
agent-workflow evaluation, covering content-equivalent pairs, citation ablations, correctness conflicts, human-validated soft and moderate quality gaps,
prompt-strength ladders, decoupled judging, and a controlled workflow-ranking probe. Across GPT-5.5, Claude Sonnet 4.6, and DeepSeek V4-Flash, stronger anti-citation
prompts reduce worse-cited wins from up to 50.5% to 0%; yet some operating points already convert validated moderate-gap decisions into Tie before the strict stress-test endpoint, while correctness-conflict accuracy remains at or above 93.0%. A second, 50-pair FinQA moderate-gap construction reproduces the qualitative frontier, and open-weight Qwen2.5-14B-Instruct-AWQ and Gemma-3-12B-IT runs reproduce the central HotpotQA frontier. TRACE-style decoupling recovers 96.5-100.0% better-plain resolution across the reported settings. Human validation separates three meanings of Tie: correct equivalence Tie, calibrated soft-boundary Tie, and resolution-destroying Tie on validated quality gaps. We frame debiasing as a measurement intervention whose bias suppression, resolution retention, Tie cost, and protocol cost must be reported jointly. The supplementary artifact contains
benchmark splits,
prompts, raw judge outputs, validation summaries, and analysis.
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Artificial Intelligence · Cross-listed from cs.LG
Beyond the Query: Do Retrieval Signals Improve Adaptive Multimodal RAG Routing?
Qiaomu Li, Qiuyuan Zhang and Nong Ming
Adaptive RAG often uses
retrieval-time signals to decide whether another
retrieval, reranking, or
multimodal step should run. We ask whether these signals add routing value once the query itself is already known. Across document, audio, and
video RAG, we compare matched query-only and query+
retrieval routers while holding the optional actions, router family, training procedure, and evaluation fixed. On the held-out final evaluation, adding the tested
retrieval signals does not produce a reliable routing improvement over the query-only baseline. Some
retrieval signals are associated with whether a later step will help, but that predictability does not consistently lead to bet- ter RUN/SKIP decisions. The main lesson is therefore methodological:
retrieval-state features should not be credited with routing value unless they improve over a matched query-only control. Our results do not show that routing or
retrieval state is generally useless; they show that the incremental value of
retrieval signals must be demonstrated rather than assumed.
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Artificial Intelligence
LifeFuse-Mem: Lifecycle-Aware State Fusion Against Temporary Overwriting for Long-Term Memory
Hanyu Zhao, Yuqian Feng, Zhenyu Song et al. (+4 more)
Long-running LLM
agents require
memory mechanisms that maintain coherent internal states across interactions. We study a lifecycle-labeled
memory setting in which write episodes provide lifecycle metadata during training, and phase-aware readout is used during evaluation. This setting reflects the need to distinguish information that should remain influential across future interactions from information that should affect only the current context. A mismatch between these lifecycles can cause temporary information to overwrite durable knowledge, leading to behavioral drift in persistent
agents. Within this setting, we introduce LifeFuse-Mem, a lifecycle-aware neural
memory framework that separates information according to its temporal commitment. LifeFuse-Mem uses dedicated
memory components and lifecycle-aware updates to allow stable and transient knowledge to evolve locally without converting temporary context into durable state. On the controlled anti-overwrite
benchmark, LifeFuse-Mem improves acquisition-controlled retention and reduces temporary overwrite; on two public long-
memory benchmarks, it remains broadly competitive. These results suggest that explicit lifecycle signals can help diagnose and mitigate overwrite in compact online
memory.
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All authors Hanyu Zhao, Yuqian Feng, Zhenyu Song, Yuanchao Cheng, Yance Jiao, Tengfei Pan, Li Du
Artificial Intelligence · Cross-listed from cs.LG
Observation-Anchored Selective Assimilation for Longitudinal Tumor-State Proxy Forecasting in Post-Treatment Glioma
Yeonjae Jung and Minwoo Shin
Post-treatment MRI in patients with glioma provides serial observations for updating patient-specific tumor-state proxy estimates, but variable appearances and trajectories complicate
forecasting. We formulate
forecasting as an observation-aware digital-twin update in which an intermediate observation anchors the patient-specific state. Among 203 patients and 594 follow-up time points, a predefined no-new-treatment criterion retained 120 of 236 candidate triplets, split into 81/24/15 training/validation/test triplets at the patient level. Each time point was represented by a continuous voxel-wise tumor-state proxy map in [0,1] derived from MRI lesion labels. A SegMamba-based single-step forecaster predicted update proposals from
multimodal source-state tensors. Observation-Anchored Selective Assimilation (OASA) retained the observed intermediate proxy as the state anchor and selectively applied updates through a validation-selected tiered case-level rule and voxel-wise soft gate. We compared initial-scan
forecasting, rollout without assimilation, latest-observation persistence, direct prediction, OASA, OASA + calibration, and morphological dilation. Checkpoints, OASA rules, and calibration thresholds were selected using validation data only. Across three seeds on 15 held-out test triplets, OASA maintained Dice at $\tau$ = 0.2 comparable to persistence (0.6071 $\pm$ 0.0025 vs. 0.6070) while yielding numerically higher Dice at $\tau$ = 0.5 (0.4269 $\pm$ 0.0079 vs. 0.3981), with a small RMSE increase. Calibration increased Dice at $\tau$ = 0.2 to 0.6178 $\pm$ 0.0025, increased false-positive (FP) support (11,836$\rightarrow$18,663), and reduced false-negative (FN) support (22,107$\rightarrow$17,536). This reflects near-threshold support calibration rather than improved biological predictive capability. Code is publicly available at https://github.com/jsudg436/longitudinal-proxy-
forecasting.
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Artificial Intelligence
Hierarchical Belief Modeling for Zero-Shot Opponent Adaptation in Partially Observable Multi-Agent Navigation
Kowei Shih, Lu Cheng, Zeyu Wang et al. (+2 more)
Lux AI Season 3 requires
agents to act under partial observability, randomized episode level dynamics, and a best of five match structure that rewards both tactical execution and fast adaptation. We present HORIZON, a hierarchical
agent that combines
symmetry aware spatial perception, dual
memory belief tracking, relic centric graph
attention, information gain driven exploration, and an opponent conditioned policy mixture. HORIZON separates short horizon control from cross match meta
reasoning, while auxiliary belief and world model objectives stabilize learning. Trained with PPO in a large scale JAX simulator, the resulting
agent explicitly infers hidden game parameters and opponent style. Experiments show consistent gains in match win rate, episode win rate, adaptation gain, and league rating over strong recurrent and feed forward baselines.
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All authors Kowei Shih, Lu Cheng, Zeyu Wang, Yeyun Xu, Kejian Tong
Artificial Intelligence · Cross-listed from cs.LG
MInTRL: Off-policy Intervention can boost On-policy RL
Mingyu Chen, Yefan Tao, Gerald Friedland et al. (+2 more)
Reinforcement learning with verifiable rewards is typically performed on-policy, keeping training data close to the current policy but limiting learning to trajectories that the policy can discover itself. Off-policy methods such as supervised fine-tuning, on the other hand, can leverage external knowledge beyond the base model's capabilities, but may suffer from large distribution shift. The key challenge is thus to expand exploration without sacrificing learnability. In this work, we introduce Minimal Intervention Reinforcement Learning (MInTRL), which expands the exploration frontier through sparse, local interventions in otherwise on-policy rollouts. During generation, a judge-intervention policy periodically reviews the current policy's output, replaces erroneous suffixes with short corrections, and immediately returns control to the policy. During training, MInTRL adopts a sequence-level advantage-regression objective that eliminates the need for importance sampling. We show that sparse, local interventions can substantially improve coverage beyond finite-budget on-policy sampling while preserving the overall on-policy nature of the resulting trajectories. Across math and code
benchmarks, MInTRL consistently outperforms standard on-policy and off-policy baselines. Ablations show that MInTRL remains effective with self-intervention and across different judge policies, while performance peaks at moderate intervention intensity, highlighting the importance of intervening minimally. These results establish minimal intervention as an effective paradigm for enhancing on-policy RL.
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All authors Mingyu Chen, Yefan Tao, Gerald Friedland, Xuezhou Zhang, Chris Kong
Artificial Intelligence
SoK: Rethinking Jailbreaking in the Era of Agentic AI: Attacks, Defenses, and Practical Consideration
Md Jueal Mia, Yanzhao Wu, Selcuk Uluagac et al. (+1 more)
Large
language models (LLMs) are rapidly evolving from conversational assistants into agentic AI systems that reason, plan, invoke tools, maintain persistent
memory, communicate with other
agents, and execute multi-step tasks. At the same time, modern models exhibit substantially stronger native safety
alignment than earlier generations on which many jailbreak attacks and defenses were originally studied. This shift raises a fundamental question: which established jailbreak-security findings remain valid in the era of modern LLMs and agentic AI? We address this question through a Systematization of Knowledge (SoK) that reframes jailbreak security around the full agentic execution pipeline. We develop unified taxonomies of attacks and defenses spanning user interaction, planning and
reasoning,
memory, tool use, and inter-
agent communication, and introduce a security–utility–efficiency evaluation framework that separates native harmful-
prompt safety,
adversarial jailbreak robustness, and
agent-level security outcomes. We further conduct a controlled empirical study of representative attacks and defenses within a common agentic framework. Our results reveal three important gaps. First, strong native
alignment does not imply robustness to
adversarial jailbreaks. Second, defense effectiveness is highly model-, attack-, and component-dependent and can come at substantial cost in over-refusal, utility, and latency. Third, low final-response attack success can mask severe intermediate compromise: planning,
memory, and tool interactions may remain unsafe even when the final response is successfully filtered. These findings motivate a shift from response-centric jailbreak defense toward cross-layer, execution-aware security that protects
agent state, component transitions, and external actions while preserving practical utility and efficiency.
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All authors Md Jueal Mia, Yanzhao Wu, Selcuk Uluagac, M. Hadi Amini
Artificial Intelligence
VRL-Bench: Benchmarking agents on computer control tasks under finite trial budgets
Yu Bai, Yukai Miao, Dawei Wang et al. (+8 more)
Learning from trial and error is a promising way to improve
language agents on complex tasks such as computer control. Reflexion introduced verbal reinforcement learning, which turns failed trials into text that guides later attempts without updating model parameters. We introduce VRL-Bench, a harness for fair evaluation of trial-and-error learning under finite trial budgets. Across three models on MiniWoB and WebShop, we evaluate updates from several prominent verbal-
memory methods spanning Reflexion and later work: each improves observed success over
memory-free retry in some settings but reduces it in others. Replay experiments show that using reflection can reduce success rates, revealing a trade-off between exploiting experience and continued exploration. We propose VEX$^2$, a verbal exploration–exploitation scheduler that uses a
language model to jointly select policies and allocate the remaining trial budget. VEX$^2$ is the only evaluated update to achieve positive observed success-rate gains over retry in all six settings.
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All authors Yu Bai, Yukai Miao, Dawei Wang, Li Chen, Yanyu Ren, Yuqian Shi, Dan Li, Ying Xiong, Chengqiu Tan, Run Zhou, Li Li
Artificial Intelligence
Beyond ID Embeddings: Process-Grounded Language Modeling for Cognitive Diagnosis
Minghang Liu, Yuanzhuo Wang, Qiang Qiu et al. (+2 more)
Cognitive Diagnosis Models (CDMs) play a pivotal role in personalized online learning. Traditional CDMs rely on discrete, ID-based
embeddings to represent students, exercises, and concepts. This paradigm diverges from the nature of learner cognition, where knowledge is not stored and retrieved as isolated symbols. As a result, CDMs suffer from semantic limitations when new exercises or concepts appear. In this paper, we propose a Process-aware
Language Cognitive Diagnosis (PLCD) framework that uses
language-derived structures as cognitive priors and response records to calibrate student posterior states. PLCD leverages large
language models (LLMs) to construct concept schemas and cognitive process graphs, and uses target-conditioned semantic
memory to retrieve historical responses that are relevant to each target exercise. A process-grounded
Language-to-Cognition Mapper with DA-MoE experts and process-level contrastive learning then maps the textual evidence into a unified cognitive space. Experimental results show that PLCD not only outperforms traditional baselines in predicting student performance but also exhibits strong cognitive transfer capabilities. These results connect the computational power of LLMs with the psychometric goal of measuring latent knowledge states, suggesting that structured
language priors calibrated by response records can improve cold-start robustness and cognitive grounding.
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All authors Minghang Liu, Yuanzhuo Wang, Qiang Qiu, Huawei Shen, Xueqi Cheng
Artificial Intelligence
Decentralized Evolution of Hexapod Gaits with Independent Leg Controllers
Gary B. Parker, John Asaro and Jim O'Connor
This paper presents a novel approach to hexapod locomotion by evolving each leg's gait independently through a decentralized evolutionary
algorithm. Using the Webots simulator and the Mantis hexapod
robot, we optimize individual leg controllers without centralized coordination, allowing emergent behaviors to drive the development of efficient, coordinated locomotion. Our decentralized method is benchmarked against cooperative coevolution, demonstrating improved efficacy in generating stable and adaptive gaits while showing interesting emergent coordination. By enabling independent
evolution of leg controllers, this method reduces the complexity of gait optimization and highlights the potential of decentralized strategies for scalable and adaptive robotic systems.
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Artificial Intelligence
OneLA: Scaling Linear-Attention Decoding to Large Beams in Generative Recommendation
Xiangrui Yang, Cheng Peng, Yunfeng Zhao et al. (+9 more)
Generative recommendation (GR) relies on large-beam decoding to generate hundreds of candidate items, creating a new scaling challenge for recurrent linear
attention. Existing linear
attention serving systems either materialize a full recurrent state for every beam or repeatedly replay shared history, incurring substantial
memory and traffic overhead. To address this, we present OneLA, a linear-
attention decoding framework that exploits the shared
prompt and short divergent suffixes of GR workloads. Specifically, OneLA represents all beam states using a single shared
prompt-derived state and compact, append-only records of their divergent transitions. Using this representation, OneLA computes only the state information required at each decoding step, without reconstructing a full recurrent state for every beam. Furthermore, OneLA uses a lightweight ancestry index to track the transition records that make up each beam's history, allowing beams to be updated without moving or copying existing records. A fused GPU kernel further reuses the shared state across beams. Our analysis shows that OneLA achieves 1.54-2.46x end-to-end decode speedups while substantially reducing recurrent-state
memory use and data movement.
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All authors Xiangrui Yang, Cheng Peng, Yunfeng Zhao, Liang Zeng, Ao Hu, Jiawei Yang, Shengzhe Wang, Jingshan Lv, Xiao Liang, Chen Yang, Jiaqiang Liu, Yiming Qiu
Artificial Intelligence
Niching Agents in The Core
Gary B. Parker, Jim O'Connor and John Asaro
The Core is a unique competitive co-
evolution algorithm that allows
agents to evolve autonomous control without utilizing a traditional fitness function. The
agents evolve via local interactions through tournament selection, crossover, and mutation, producing offspring by evolving better controllers. Previous works have shown The Core's ability to evolve
agents capable of combat and
navigation in the Xpilot
video game. This research expands upon that premise by niching
agents to specific subsets of the original environment The Core was tested in. Our results demonstrate the niched
agents capacity for success over
agents niched to the entire system and
agents niched to different sub-environments.
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Artificial Intelligence · Cross-listed from cs.CV
UFO: Chain-of-Evaluation for Omni-Condition Alignment in Multi-Modal Image Generation
Danning Zhang, Yijing Lin, Shuhan Zhuang et al. (+4 more)
Multi-modal image generation, particularly subject-driven customization, has garnered growing
attention in recent years. Despite the rapid advancement of generative models, their evaluation remains largely lagging. Existing methods, whether
embedding-based or Multi-modal Large
Language Model (MLLM)-based, evaluate
alignment with each modal condition in isolation, which contradicts the simultaneous condition
alignment objective of multi-modal image generation, leading to poor consistency with human judgments. To address this challenge, we propose UFO, the first unified framework for omni-condition
alignment simultaneous evaluation. Specifically, UFO introduces a novel Atomized Chain-of-Evaluation paradigm, i.e., it first decomposes omni-condition
alignment into a sequential chain of fine-grained, disentangled Atomic Evaluation Units (AEUs), categorizes them into distinct modality-relevance classes, and then employs general or dedicated functional calls for accurate verification of different AEU types. Experimental results demonstrate that UFO achieves the highest correlation with human evaluation preferences, delivering an average improvement of 15.25%. Furthermore, we present UFO-Bench, a dedicated
benchmark designed to holistically evaluate the performance of existing customization models under the diverse mutual interactions of textual and visual conditions.
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All authors Danning Zhang, Yijing Lin, Shuhan Zhuang, Mengqi Huang, Shaojin Wu, Shancheng Fang, Zhendong Mao
Artificial Intelligence
Is Gaussian Splatting Becoming Neural Again? A Taxonomy and Controlled Study of Learned Parameterization
YuanHang Wang, Xin Cao and Yi Zhang
Three-dimensional Gaussian Splatting (3DGS) combines explicit primitives with efficient rasterization, yet recent systems increasingly use neural networks to generate or share Gaussian parameters. We characterize this trend along five axes: attribute decoding, spatial sharing, view-conditioned decoding,
topology generation, and amortized inference. An analysis of 19 representative methods shows that these choices address different limitations and cannot be reduced to a binary neural label. We also isolate three forms of neural parameterization in a controlled mip-NeRF 360 study. Sharing appearance and opacity improves reconstruction quality, while decoding
geometric structure offers no further gain. The evidence favors selective neuralization: shared functions help when they capture reusable correlations without sacrificing the local
geometric freedom of explicit splats.
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Artificial Intelligence
BlueLM-GUI Technical Report: A Real-Device-Centric Flywheel for Self-Improving Mobile GUI Agents
Tong Ye, Kunyang Han, Guozhi Wang et al. (+40 more)
Mobile GUI
agents are shifting from multi-module frameworks to native models trained end-to-end, yet industrial deployment faces three persistent gaps. Sandbox training produces a distribution mismatch with production environments; expensive real-device failures remain underutilized; and fixed
benchmarks saturate, losing the power to guide iteration. We present BlueLM-GUI, a 35B-A3B mobile GUI
agent built as a real-device-centric flywheel that closes these gaps through three principles. Every Sample Matters: a dual-track pipeline with Heterogeneous Triple-System Consensus evaluation and an Error Correction & Derivation Module salvages every trajectory into usable supervision. Every Rollout Is Real: a three-stage recipe—continual pre-training, supervised fine-tuning, and agentic reinforcement learning on hundreds of real phones—grounds every rollout in real production environments, so the capability the model learns transfers directly to deployment. Every Query Evolves: a quota-driven
benchmark methodology with three orthogonal axes enables precise attribution and allows the
benchmark to be systematically upgraded as the model improves. BlueLM-GUI achieves 87.4 on MobileGUI-VBench, surpassing the best closed-source model by 5.1 points, and 84.9 on AndroidWorld, the best result among open-source models and competitive with closed-source models. These results demonstrate that grounding model training and iterative improvement in both real devices and the three Every principles yields strong, robust, and transferable mobile GUI capability.
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All authors Tong Ye, Kunyang Han, Guozhi Wang, Longqiang Luo, Zhifeng Ding, Yongxiang Zhang, Xiaolei Shen, Yuxuan Zhang, Zhuping Zhang, Tao Xu, Yue Pan, Yucheng Zhao, Yupei Hu, Yuanjiang Ouyang, Danfeng Shen, Runqi Lin, Hongda Cai, Zhaoxiong Wang, Mengjia Yan, Yingjie Zhong, Chen Zhou, Zeyu Zhang, Xuwen Zhu, Penggang Shi, Mingcheng Luo, Ziyang Wu, Min Jin, Mingfu Shen, Zairong Xu, Fan Zhang, Hao Wang, Liang Liu, Zhulin Xie, Lijun Yao, Xiao Liang, Liangmin Wen, Liqiang Feng, Feilong Wu, Min Hu, Min Chen, Guanjing Xiong, Xiaohu Ruan, Xiaoxin Chen
Artificial Intelligence · Cross-listed from eess.SP
RF-VoID: Towards Bandwidth-Efficient Exterior Tile Void Detection via Narrowband Radio-Frequency Representation Learning
Xinyan Chen, Ruiqin Ma, Shunsuke Shoda et al. (+5 more)
Hidden debonding behind exterior ceramic tiles is a falling-tile hazard, and millimeter-wave radar offers a non-contact way to find it. Conventional interpretation first reconstructs a range profile, so its reliability is bounded by the available bandwidth, yet bandwidth is what sets the cost, the acquisition time, and the regulatory footprint of a deployed system. This work asks whether that bandwidth can be traded for computation. A 4-40 GHz stepped-frequency system scans twelve exterior-wall specimens containing 0.5-1.0 mm air voids at different depths and interfaces, and the bandwidth dependence of A-scan, B-scan, and C-scan interpretation is analyzed to establish the resolution bound. RF-VoID is then proposed, which decides directly on the narrowband complex response: the sub-band is kept in its measured frequency order with amplitude and phase alongside the in-phase and quadrature channels, a dual-branch encoder reads it along the physical frequency axis using relative position encoding and a distance-dependent locality bias, and an inspection-oriented objective handles the class imbalance and the asymmetric error cost of facade screening. Under a mixed-sample protocol the method attains 98.61% accuracy and a 95.84% F1-score with 0.5 GHz of bandwidth, a seventy-two-fold reduction relative to the full sweep, without range-profile reconstruction, deconvolution, or depth-slice selection; on specimens held out entirely from training it remains the strongest of the compared models, with a mean macro F1-score of 62.12% at 0.5 GHz that rises to 68.57% at 1 GHz.
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All authors Xinyan Chen, Ruiqin Ma, Shunsuke Shoda, Changyu Zhou, Ryo Natsuaki, Akira Hirose, Jianfei Yang, Li Yi
Artificial Intelligence
Toward Robust Personalized Alignment for LLMs: Mitigating Persona Drift in Multi-Turn Dialogue
Youyuan Zhang, Siyuan Li, Fangming Liu et al. (+1 more)
Persona drift remains a central challenge for personalized
language models, as user profiles evolve over long interactions rather than remain permanently fixed. Models must therefore revise persistent persona states when preferences genuinely change, while avoiding updates driven by transient, ambiguous, or unresolved observations. We propose CORE, which separates turn-local evidence from persistent persona-state revision and selectively updates grounded user preferences through uncertainty-aware belief revision. We also introduce PERSIST, a held-out post-anchor
benchmark for persona-state robustness under sequential interaction stress, covering ambiguity, conflict, and controlled social influence. Across ALOE, PersonaChat, and PERSIST, CORE improves personalized
alignment and robustness, with complementary gains in normalized closed-slot state fidelity. Human evaluation and mechanistic controls further support explicit update control beyond stronger generation or persistent
memory alone.
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All authors Youyuan Zhang, Siyuan Li, Fangming Liu, Jing Li
Artificial Intelligence · Cross-listed from cs.CL
ORQA: An Occupation-Realistic Question and Answer Framework for LLM Professional Knowledge
Shreyas Krishnan, Serina Chang and Abhishek Nagaraj
We present ORQA, a method for testing occupation-level knowledge in large
language models. Prior methods either map abstract LLM skills to occupations via task definitions or utilize expert knowledge which is difficult to obtain at scale and expensive. ORQA complements both of these methods by connecting O*NET occupations to trusted occupation-specific websites (such as regulatory agencies, licensing bodies, professional organizations, and government publications) and converting these into source-traceable question-answer pairs. A combination of an automated pipeline and human review produces a set of high quality questions about occupations. The question set created via our method covers 116 occupations from all 21 major groups in the SOC, with 480 questions sourced from 187 different websites. Each question is designed to probe a real-world skill question that is relevant to the occupation in question. We test 15 state-of-the-art frontier and open-weight models via this method. Claude Opus 4.6, GPT-5.4 and Claude Sonnet 4.6 all perform the best at approximately 58-62% while smaller open-weight models achieve approximately 33-41% performance. Performance varies significantly across occupations. Healthcare-related occupations achieve the highest performance (78%) while Office and Administrative Support achieve approximately 40%. Performance on individual occupations (e.g. Sheet Metal Workers and Fish and Game Wardens) is essentially zero. We also find that open-ended questions and weighting by wage bill do not significantly affect the ranking of models on this
benchmark. We believe that leveraging existing trusted occupation-specific information to test LLM knowledge in professional domains may be a scalable and useful method for evaluating occupation-level AI performance in the future. Results and data are available at orqabench.org.
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