Artificial Intelligence
Agentic Semantic Sensing for Resource-Adaptive AI-RAN
Zhongqin Wang, Xiaoqi Zhang, Nan Yang et al. (+3 more)
Semantic sensing (SemS) acquires task-relevant information rather than reconstructing complete physical information. Existing SemS formulations typically operate open loop: sensing configurations and observation schedules are fixed before inference and cannot respond to evolving task-level evidence. We propose Agentic SemS, a closed-loop framework for AI-enabled
radio access networks (AI-RANs) that controls sensing within a communication-feasible profile set. A profile-conditioned causal
Transformer updates the semantic belief from streaming observations, while key-value caching enables efficient state updates across profile changes without repeatedly processing the complete history. A semantic utility network estimates the task-level benefit of acquiring the next observation block under each feasible profile after accounting for sensing cost. The resulting continuation utilities jointly support next-profile selection and semantic early exit, adapting sensing configuration and duration to evolving evidence. The expected semantic gain is further related to conditional mutual information, providing a value-of-information interpretation of continued online sensing. Experiments on Widar3.0 with six emulated sensing profiles show that, in comparison with full-sequence High, the resource-efficient Agentic setting reduces normalized cumulative sensing cost by 25.33% while achieving 85.79% Macro-F1. At the same utility checkpoint, semantic early exit provides a further 12.35% cost reduction over adaptive sensing without early exit, with a 0.97-percentage-point Macro-F1 decrease.
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All authors Zhongqin Wang, Xiaoqi Zhang, Nan Yang, Kai Wu, J. Andrew Zhang, Y. Jay Guo
Artificial Intelligence · Cross-listed from cs.CL
One Step at a Time: Trading LLM Autonomy for Process Predictability
Hans Schabert and Christoph Peters
Organizations automating operational processes need more than a correct outcome: they need to predict how a process will run, know which one actually ran, and inspect it step by step. When an
agent is the executor that predictability is normally lost: the prescribed procedure goes into the system
prompt, and only a final answer comes back. We deliver the procedure step by step over the Model Context Protocol (MCP) instead: a server releases one step at a time, the
agent executes it, and each step returns a structured step_output. This trades autonomy for predictability, and two properties then follow by construction, independent of the executor. The execution path is prescribed before the run, so the process is predictable in advance rather than reconstructed afterwards; and the completed step records form a machine-readable execution log that downstream tooling can audit and optimize step by step. Evaluating 15,475 trials across 13 SOP-Bench domains and four open-weight executors from frontier (Kimi K2.5) to lightweight (Ministral 3 8B), we find step-level delivery makes the executed process predictable and inspectable for every executor, and additionally raises accuracy when the executor is small. Across all four, process adherence rises significantly (76-95% to 95-99%) and ungrounded answers (correct outputs produced without executing the SOP) near-vanish, falling from 2.1-4.5% to 0.2-0.3% of trials (all 95% CIs exclude zero); under
prompt-based delivery, 31-49% of correct answers on know_your_business bypass the SOP entirely, even for the frontier executor. Accuracy is where the executor's capability enters: the lightweight executor gains +6.5pp grounded accuracy because supplying the process externally removes a reconstruction burden it cannot carry, while capable ones trade a small raw-accuracy decrement for a predictable, auditable process.
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Artificial Intelligence
Do I Need the Cloud? Uncertainty-Aware Step-Level Handoff for Small Language Model Agents
Abolfazl Younesi
Small
language models (SLMs) are attractive as local
agent controllers because they reduce remote inference, latency, and deployment footprint, yet structured tool errors can cause an
agent step to fail. Existing routers typically select a model once per query. However,
agents expose sequential decision points whose difficulty dynamically changes based on intermediate observations. We propose STEPGATE, an uncertainty-aware handoff framework that scores each local SLM action and selectively escalates challenging steps to a stronger model. On a 52-task held-out single-step BFCL-derived test split, the Qwen2.5-1.5B/7B pair attains 82.7% task success with 30.8% escalation, versus 67.3% local-only and 75.4% random escalation (which uses 33.8% escalation). In a separate multi-turn evaluation, STEPGATE achieves 69.0% trajectory success and 84.0% action success using only 30.0% cloud actions, compared with 48.0%/70.5% local-only, 60.0%/78.2% random escalation, and 57.0%/77.1% query-level routing (strong-only achieves 82.0% trajectory success at 100% cloud actions). These results suggest that step-level escalation recovers a large share of the performance gap to the stronger Qwen2.5-7B backend at a matched cloud-action rate while transmitting fewer
tokens remotely. However, our evaluation is limited to one model family, a single stronger backend, and scripted tasks. Furthermore, the test sets are small, multi-turn comparisons rely on paired intervals and statistical tests, and our risk tiers serve as research annotations rather than formal safety guarantees.
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Artificial Intelligence
ThinkFuse: Trajectory-Aware Test-Time Fusion for Small Reasoning Models
Myunghoon Kang, Jungseob Lee, Jaehyung Seo et al. (+1 more)
Small
reasoning models (SRMs) have shown strong performance on complex
reasoning tasks by generating extended chain-of-thought trajectories, but they often fail to recover once their
reasoning enters an erroneous path. Existing test-time fusion methods rely on local fusion signals to determine when to trigger fusion, which can be misled by transient uncertainty fluctuations and may reinforce unstable
reasoning trajectories. We propose ThinkFuse, a training-free test-time fusion framework that selectively intervenes in unreliable
reasoning segments. ThinkFuse compares segment-level uncertainty shifts with trajectory-level uncertainty trends to identify unstable
reasoning points and fuse auxiliary
reasoning paths into the primary model's trajectory. Extensive experiments demonstrate that ThinkFuse outperforms baselines on mathematical and knowledge-intensive
reasoning benchmarks, with consistent gains across model-family combinations, and remains robust with a smaller primary model. Our analysis shows that ThinkFuse requires fewer fusion triggers and generates fewer
tokens, highlighting the efficiency of selective triggering. Our code is available at https://github.com/js-lee-AI/ThinkFuse.
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All authors Myunghoon Kang, Jungseob Lee, Jaehyung Seo, Heuiseok Lim
Artificial Intelligence · Cross-listed from cs.HC
Novice Reliance Calibration in AI-Assisted Decision Making: The Role of Explanations and Self-Assessment
Eun Jeong Kang, Peter Duan and Swati Mishra
Artificial Intelligence (AI) tools are widely used to support decision making in tasks and domains where no immediate performance feedback is available. In these settings, users cannot learn to adjust their reliance behavior over time through trial and error. However, little is known about how novice users calibrate reliance on AI when external feedback is unavailable, or whether AI explanations can support calibration in its absence. We introduce reliance calibration as an organizing construct for studying how novice users dynamically adjust reliance behavior, and examine how AI explanations and meta-cognitive self-assessment shape it. Through a between-subjects study with 110 participants completing a clinical entity extraction task with AI assistance and limited performance feedback, we observe that novice users exhibit systematic drift toward over-reliance in the presence of explanations, while higher self-reported task understanding is associated with more selective reliance behavior. These results extend reliance calibration research into human-AI collaboration contexts without real-time performance signals and present actionable guidelines on designing AI tools that must support appropriate reliance in these settings.
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Artificial Intelligence
Thin Evidence, Thick Priors: How Language Models Substitute Identity for Missing Financial Facts
Saanvi Khetan and Sankar Balasubramanian
People increasingly ask large
language models what to do with their money, yet seldom describe their finances in full. This paper asks what a model does with the gap. Holding finances fixed and changing only who the investor is said to be, we grade the financial evidence in the
prompt from eight facts to none and measure how far the recommended equity allocation moves. Across 96,600
prompts to Llama-3.1-8B-Instruct, built from 100 financial profiles, 138 personas and seven disclosure conditions, the average gap between two personas with identical finances rises from 4.78 percentage points at full disclosure to 10.34 points with no financial facts. A two-way cluster bootstrap counting duplicated
prompts once places the ratio at 2.16 (95% interval 1.69 to 2.79), and the rise is already 1.69-fold with a single fact left. Identity explains 5% of within-profile variation in advice at full disclosure and 96% with no disclosure. Household size is the only attribute whose influence grows reliably as evidence is withdrawn. Once standard errors are clustered on the persona, the unit to which identity was assigned, most attribute-specific interactions reported in the conference version lose significance, and gender instead appears as a small standing gap that full disclosure does not close. Stating risk appetite alone brings the swing into the range seen with two to seven generic facts. With no facts, the model's one-line rationale cites incomes, debts and savings it was never told, and these invented finances turn adverse more often for larger households. Inside the network, gender is linearly decodable at every layer, and ablating the gender direction at five layers leaves the aggregate identity swing unchanged. Advisory systems built on such models should be audited at the disclosure levels users actually reach, and judged across the whole identity space rather than one attribute at a time.
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Artificial Intelligence · Cross-listed from cs.LG
The Geometry of Empowerment
Catherine Ji, Vivek Myers, Sergey Levine et al. (+1 more)
Empowerment captures the capacity for an
agent to actively control its environment. While conceptually appealing as an information-theoretic quantity, the connection between empowerment and structurally central states that provide broad access to future outcomes has remained an open question. In this work, we link empowerment maximization and skill-learning methods to provide new geometries for interpreting and analyzing empowerment. Our analyses answer longstanding open questions on the connections between empowerment and structural centrality. Our analyses also reveal distinctions between information and reward geometries, highlighting important theoretical implications to build scalable empowerment-maximization methods. Website and code can be found at https://empowerment-geometry.github.io/.
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All authors Catherine Ji, Vivek Myers, Sergey Levine, Benjamin Eysenbach
Artificial Intelligence · Cross-listed from cs.LG
ServeLearnBench: How Well Can Agents Self-Improve from Serving Experience?
Haizhong Zheng, Yizhuo Di, Ranajoy Sadhukhan et al. (+2 more)
Large
language model
agents are increasingly deployed to perform complex tasks in real-world environments. However, the knowledge required for correct behavior in these environments is often implicit, undisclosed, and subject to change over time. Recent continual-learning harnesses seek to address this challenge by enabling
agents to improve from serving experience. Yet the effectiveness and limitations of these methods are not yet well characterized. Existing
benchmarks provide only partial coverage: some explicitly provide the target knowledge, others assume a static environment, and those that support continual adaptation remain limited in scale and knowledge diversity. To enable systematic evaluation, we formalize an evolving-environment streaming
dataset (EESD), in which
agents must infer, apply, and revise latent environment knowledge from interaction and outcome feedback as hidden policies evolve, and introduce ServeLearnBench, spanning retail support, banking, and sales-pitch generation with 53 environment windows and 7,718 tasks. We evaluate five learning harnesses (RAG, Mem0, SkillOpt, Continual Harness, and
Prime) across six models (GPT-5.6 Terra, Opus 5, Kimi K3, GLM-5.3, DeepSeek V4.1 Flash, and GLM-5.3 Flash), covering 28 model-harness pairs and 252 learning runs. Our evaluation reveals three main findings: a substantial gap remains between task capability and learning from experience; continual adaptation is costly and can degrade already-correct behavior; and insufficient exploration emerges as a key bottleneck to effective adaptation. Overall, ServeLearnBench provides a controlled testbed for diagnosing these limitations and tracking progress toward
agents that continually and reliably improve through serving experience.
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All authors Haizhong Zheng, Yizhuo Di, Ranajoy Sadhukhan, Shuowei Jin, Beidi Chen
Artificial Intelligence
Illusory Pattern Perception Drives Spurious Inference in Large Language Models
Peihua Mai, Zhuoyan Shao, Xinbao Qiao et al. (+3 more)
Illusory pattern perception is a well-documented human cognitive tendency to infer meaningful relationships in data that is actually random. Such a tendency, often described as "connecting the dots" where none exist, can result in systematic
reasoning errors. This paper investigates whether Large
Language Models (LLMs) exhibit such perceptual tendencies, which can lead to systematic errors in downstream applications. To our knowledge, this work presents the first systematic study of illusory pattern perception in LLMs, adapting classic psychological paradigms to three tasks with direct empirical comparison to human behaviors. We find that LLMs frequently exhibit stronger illusory pattern perception than humans. In particular, models tend to over-associate frequent positive attributes with majority groups or large organizations, and show increased tendencies to construct causal narratives from ambiguous events. To uncover the mechanism behind these behaviors, we develop a feature interpretability framework based on Sparse Autoencoders (SAEs) to analyze internal representations. Our results reveal that holistic frequency perception and analytic cognitive orientation are linked to the emergence of illusory perceptions. These findings highlight a previously underexplored cognitive-like illusion that may affect the reliability of LLM
reasoning. Code available at https://github.com/NusIoraPrivacy/illusory.
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All authors Peihua Mai, Zhuoyan Shao, Xinbao Qiao, Meng Zhang, Xinyue Zhou, Yan Pang
Artificial Intelligence
OOPMAS: Object-Oriented Multi-Agent Systems for Query-Level Workflow Generation
Qi Cheng, Shengyu Chen, Wei Cheng et al. (+4 more)
Multi-
agent systems (MAS) powered by large
language models have shown strong performance across code generation, mathematical
reasoning, and question answering. However, existing methods for automating MAS design mostly operate at the task level, producing a single fixed workflow per
benchmark that is applied uniformly to all queries. This assumption fails under realistic conditions. Query difficulty varies widely within a task, and real-world workloads mix heterogeneous task types. We introduce OOPMAS, a training-free framework that generates both the
agent set and the coordination workflow at the granularity of individual queries.
Agents are represented as object-oriented class definitions with dedicated roles, tools, and persistent state, and workflows are expressed as executable main functions over these
agent objects. A dynamic skill library accumulates structured lessons from execution feedback across optimization rounds, enabling in-context improvement without any gradient updates or fine-tuning. On a mixed-task
benchmark of queries spanning code, math, and QA, OOPMAS achieves 89.6% accuracy, outperforming the strongest baseline by 18.1 percentage points. A model-swap study across four LLM backbones shows consistent scaling, reaching 92.4% with the strongest model.
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All authors Qi Cheng, Shengyu Chen, Wei Cheng, Yiqun Xie, Xiaowei Jia, Haoyu Wang, Haifeng Chen
Artificial Intelligence
Attacca: Goal-Directed Control under State Continuity for Long-Horizon Embodied Agents
Gyusik Seo and Jaehong Yoon
A central capability of embodied
agents is to accomplish complex objectives through sequences of interdependent tasks. Yet existing visual goal-conditioned policies underlying these
agents are typically evaluated on isolated interactions where the target is already visible, and thus do not capture the conditions that arise during continuous long-horizon task execution. In such settings, each task begins from the state left by the previous one: the
agent may end at a different position and orientation, the world may have been modified, and the next interaction target may lie outside the current field of view. As a result,
agents relying on such policies may struggle to proceed to the next task when they cannot ground their target in the current observation. To address this challenge, we propose Attacca, a new approach that trains visual goal-conditioned policies on complete search-to-interact trajectories using goal images decoupled from the execution environment. Attacca uses context-decoupled goal sampling to pair each demonstration with a class-compatible masked goal image from another world, removing direct scene and pose correspondence. It learns dense current-view grounding through a target-mask prediction head, providing auxiliary supervision beyond action imitation. We further introduce behavioral-phase conditioning that teaches the policy to distinguish Search, Approach, and Interact stages and adapt its control as execution progresses. We evaluate Attacca on multiple short- and long-horizon embodied tasks in Minecraft. Our method achieves 39.0-47.5% clean success, improving over the strongest baseline by 1.7-2.4x. On long-horizon tasks, it attains 54%, 30%, and 28% completion, yielding up to a 7x improvement.
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Artificial Intelligence
Persistent Memory in Multi-Agent LLM Inference: What It Costs, What It Buys, and When You Can Tell
Hochan Son, Kyungdoe Han, Jaehan Koh et al. (+3 more)
Decomposing long-context inference across cooperating
agents bounds the active KV cache per call rather than total evidence, which matters when KV-cache
memory binds. Many such systems add a persistent tier storing and recalling
reasoning traces, usually validated by an ablation reporting an accuracy gain. We measure both on one three-tier
agent architecture. Decomposition delivers: peak KV working set of 14.3 MiB per query against 35.5 and 35.3 MiB for single-pass and
retrieval-augmented baselines. The persistent tier does not: across eight controlled
dataset pairs at n=100 per arm it costs +0.368 MiB [+0.167, +0.590] of peak cache and produces no detectable accuracy change (+0.015, 95% CI [-0.011, +0.046]). We argue the null is structural: single-question
benchmarks supply each item with its own evidence and score it independently, and correctness requires resetting stored traces between conditions, so recall has nothing informative to retrieve. Reaching it took four measurement corrections – three inflating the apparent benefit, the fourth making an effect that size look resolvable – none visible in the results table. We give the conditions an
agent-
memory ablation must satisfy and detection procedures that need no knowledge of the specific defect.
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All authors Hochan Son, Kyungdoe Han, Jaehan Koh, Xiaowu Dai, Wenlu Xu, Guang Cheng
Artificial Intelligence
Quantization Effects on Tool-Failure Recovery Vary Across Prompts and Evaluation Designs
Yuhe Hu
Post-training quantization reduces the cost of deploying
language-model
agents, but its effect on recovery from temporary tool failures can depend on how recovery is evaluated. We compare 8-bit and 4-bit variants of Llama-3.1-8B-Instruct and Qwen2.5-7B-Instruct on twenty deterministic tool-use tasks and five
prompts. The 8-bit-4-bit recovery comparison changes direction across
prompts and evaluation targets. On tasks that both variants complete without faults under the same
prompt, the difference ranges from 0 to +20.2 percentage points for Llama and from -50.0 to +35.0 points for Qwen. Full-pipeline point estimates favor 8-bit Llama under all five
prompts, whereas the Qwen comparison changes direction across
prompts. The evaluation target can also reverse the result. For Llama under one
prompt, scoring each variant only on its own clean-passing tasks favors 4-bit by 17.5 points; scoring the same tasks for both variants gives no difference, while scoring the full pipeline favors 8-bit by 28.3 points. Executor leniency is a third such choice. Rescoring the same logs with strict output parsing, which 8-bit Llama violates far more often than 4-bit Llama under that
prompt, turns that +28.3 into -15.0 while leaving Qwen essentially unchanged. These findings show that one
prompt, one screened task set, and one scoring policy do not establish a stable conclusion about quantized-
agent robustness. Evaluations should compare variants on matched tasks, report full-pipeline success for deployment decisions, state the scoring policy, and quantify uncertainty across tasks rather than injected fault sites.
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Artificial Intelligence · Cross-listed from cs.LG
Towards One-for-All Foundation Model for Attributed Graph Clustering
Yunhui Liu, Xudong Jin, Kang Zhang et al. (+5 more)
Attributed graph clustering aims to discover node groups by jointly exploiting node attributes and graph
topology, yet its unsupervised nature makes model selection and adaptation inherently difficult. Existing methods typically train and tune a separate model for each input graph, leading to costly and fragile pipelines that often fail to transfer across graphs with different feature spaces, structural patterns, and attribute-structure correlations. In this paper, we study a one-for-all alternative: can a single model be trained once and directly applied to diverse attributed graphs without graph-specific training, fine-tuning, or hyperparameter search? We propose OFAG, a foundation model for attributed graph clustering. Building upon Prior-data Fitted Networks, OFAG learns a reusable clustering inference strategy from synthetic attributed graphs generated under broad priors over latent clusters, node attributes, and graph structures. To handle incompatible feature spaces across graphs, OFAG adopts a dimension-agnostic signal-wise graph encoder that treats each feature channel as a graph signal and models its response to shared graph filters. The model is trained with a hyperspherical clustering objective, producing clustering-friendly node representations in a single forward pass at inference time. On ten
datasets, one frozen OFAG model achieves the best mean performance and average rank across NMI, ACC, ARI, and F1, while completing all ten
datasets in 12.43 minutes total—over 6* faster than the second-fastest baseline and nearly 28* faster than the second-best on clustering quality. Our code and pretrained checkpoint are available at https://github.com/Cloudy1225/OFAG, allowing practitioners to directly apply OFAG to their own attributed graph
datasets without additional training or tuning.
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All authors Yunhui Liu, Xudong Jin, Kang Zhang, Danshuo An, Yu Xing, Te Song, Jia Liu, Tieke He
Artificial Intelligence
Farbod Tavakkoli, Gregory Diamos, Kenneth Church et al. (+14 more)
We present Open Telco (OTel), an open telecom AI resource that releases derived telecom
datasets for
retrieval, reranking, instruction tuning, and safety/abstention, together with 30 full-parameter post-trained baselines spanning 10
embedding models, 3 rerankers, and 17
language models. The community has already engaged substantially with the resource: as of May 3, 2026, the released models have been downloaded over 16 million times and the project has received 157+ pieces of media coverage worldwide. Building on prior open telecom
datasets and
benchmarks, OTel provides documented telecom data sources, held-out evaluation partitions, trained
embedding models, rerankers, context-grounded LLMs, and safety/abstention data in one unified resource. Each baseline starts from an open-weight model and is post-trained on OTel-derived data using an open training recipe, then evaluated on held-out OTel evaluation partitions. OTel post-training improves performance across all three model families:
embedding retrieval reaches 93.1% NDCG@10, reranking reaches 0.947 MRR@10, and
language-model correctness reaches 87.8%. We release OTel as a reproducible starting point and invite the community to expand the data, improve
embedding and reranking models, and build stronger context-grounded telecom LLMs.
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All authors Farbod Tavakkoli, Gregory Diamos, Kenneth Church, Roderic Paulk, Jorden Terrazas, David Kanter, Mark Austin, Imtiaz Karim, Syed Ali Raza Zaidi, Lina Bariah, Merouane Abdelkader Debbah, Ali Maatouk, Louis Powell, Nikolaos Vasiloglou, Ashish Vaswani, Somanshu Singla, Adarsh Chaluvaraju
Artificial Intelligence · Cross-listed from cs.CL
No Transformer Beats Six Covariates: Long-Horizon Prediction of Depressive Symptoms from Childhood Essays
Daniel Kua, Emrul Hasan, John-Jose Nunez et al. (+1 more)
Natural
language processing (NLP) models can detect depression-related
language in text written near the time symptoms are measured, but whether pretrained
transformers can predict depressive symptoms from text written twelve years earlier is largely untested. In the National Child Development Study, a British birth cohort, we predict probable depressive symptoms at age 23 from essays the same people wrote at age 11. Our baseline, a logistic regression on six childhood
covariates, outperforms every text model that sees only the essay: seven fine-tuned
transformers, a bag-of-words model, frozen
embeddings and four zero-shot large
language models. Its area under the receiver operating characteristic curve (AUC-ROC) is 0.737 against 0.670 for the best
transformer on the primary seed, and no added text score detectably raises the baseline's AUC-ROC. None of the five domain-pretrained
transformers detectably beats its general-domain control after Bonferroni correction. For long-horizon prediction, the baseline remains the model to beat.
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All authors Daniel Kua, Emrul Hasan, John-Jose Nunez, Frances Chen
Artificial Intelligence
ST-Bench: A Spatial-Temporal Benchmark for Multi-Agent System Generation on Scientific Research Tasks
Qi Cheng, Rongchao Dong, Shengyu Chen et al. (+8 more)
The rapid progress of LLM-based multi-
agent systems (MAS) has shown that they largely outperform single
agents on coding, math, and QA tasks, where executable tests provide a binary success signal. Whether this advantage transfers to real scientific data analysis remains untested. We introduce ST-Bench, a
benchmark designed to answer two questions: whether MAS outperform single
agents on complex scientific data analysis tasks, and if so, by how much and at what additional cost. ST-Bench contains 100 data science tasks adapted from published Earth science studies across hydrology, agriculture, and wetland methane research, expanded into 2,067 queries grounded in additional published studies and validated by domain experts. Using ST-Bench, we evaluate five recent MAS generation methods under two training protocols, against single-
agent baselines on the same GPT-5 backbone. Nine of the ten MAS configurations exceed the cheapest single-
agent baseline, with the strongest reaching nearly three times its composite score. This gain is primarily attributable to coverage: trained workflows produce realistic numerical metrics on a larger fraction of queries, while the quality of those metrics, conditional on producing realistic output, is comparable to that of the single-
agent baseline. The strongest configuration requires approximately four times the single-
agent inference time, whereas a more economical workflow captures the majority of the benefit at less than twice the cost. MAS specialization confers measurable benefit on scientific data analysis, but the benefit is conditional rather than universal.
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All authors Qi Cheng, Rongchao Dong, Shengyu Chen, Licheng Liu, Dan Lu, Zhengzhang Chen, Wei Cheng, Yiqun Xie, Haifeng Chen, Xiaowei Jia, Haoyu Wang
Artificial Intelligence · Cross-listed from cs.IR
Contrastive Learning for Aspect Representation towards Explainable Recommendation
Emrul Hasan and Chen Ding
In this work, we propose a novel recommendation model, CLARER (Contrastive Learning for Aspect Representation towards Explainable Recommendation) that integrates aspect features learned from textual reviews with rating information to improve the accuracy and explainability of recommendations. Our proposed framework learns user and item representations by combining rating-based features and aspect-based features from reviews. Specifically, rating-based features are learned through a multi-layer perceptron (MLP) model, while aspect-specific review representations are learned using a
transformer encoder to capture the semantic information and contrastive learning to better distinguish user preferences. To provide explanations, we train a
transformer decoder, using the final representations of users and items from both rating and aspect-based features as context. Experimental results in three
benchmark data sets demonstrate that our model achieves superior performance compared to baseline methods in both recommendation (accuracy) and explanation generation.
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Artificial Intelligence · Cross-listed from cs.CV
Later Is Better: Token Reduction for ViTs Under Distribution Shift
Hyeongheon Cha, Hyungjun Yoon and Sung-Ju Lee
Training-free
token reduction accelerates
vision transformers by removing redundant
tokens across layers, recovering most of the original accuracy at a fraction of the compute. These methods, however, are designed and evaluated primarily on clean data, and under real-world distribution shift their accuracy gap to the uncompressed model widens with the removal rate. We show that this gap is governed by the reduction schedule, the depth profile of removal, usually left fixed as an implementation detail. Concretely, we introduce a one-parameter late-concentrated power-law schedule that consistently improves out-of-distribution accuracy over flat at no extra inference cost. On ImageNet-C with DeiT-S, the late schedule closes 83% of that gap at a 26% compute reduction (+1.17pp), and 99% of it at a lighter 7% reduction (+0.26pp). The gain cannot be attributed to retaining more
tokens or using extra compute: held to flat's compute, the late schedule removes more
tokens in total and leaves fewer
tokens at the end, yet still wins. Single-layer probes point to a mechanism: earlier reductions perturb features that pass through more remaining layers, front-loading reduction error in depth. The effect is broad, holding across five
token-reduction methods (ToMe, EViT, ATS, ATC, PiToMe), nine backbones, all ImageNet-C corruption types, eight further shift suites, and two further modalities,
video and
vision-
language QA. It is also specific to shift, still positive on clean and rising monotonically to ~4x that at the highest severity 5. The schedule keeps its gain under six test-time adaptation methods, and needs no per-input or per-domain tuning.
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Artificial Intelligence · Cross-listed from cs.CL
From Evidence to Action: How Tool-Using Agents Fail
Hongzhan Lin, Shidong Cao, Ziyang Luo et al. (+3 more)
Tool-using
agents make consequential changes to external state, yet correct outcomes do not guarantee that their actions were supported by evidence established beforehand. We study where this evidence-to-action chain breaks as
agents move from deciding whether to act to executing single actions and dependent workflows. Across ten model-harness configurations, strong static action assessment can coexist with much weaker interactive execution. Failures often begin before execution:
agents stop with incomplete investigation or act before required evidence is established. Once required evidence is obtained, single-action execution is usually reliable, while multi-action workflows additionally expose unresolved prerequisites and incomplete execution. For this analysis, we introduce SafeActBench, comprising 656 cases across six operational domains and five protocols that progress from static action judgment and investigated non-action to single- and multi-action workflows. A provenance-bound Evidence Ledger and deterministic trajectory evaluator track what information was established, when actions occurred, and whether downstream dependencies were satisfied. These results show that failures arise not only from missing information, but also from how
agents use established evidence when deciding and executing actions.
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All authors Hongzhan Lin, Shidong Cao, Ziyang Luo, Wenhao Chai, Mong-Li Lee, Wynne Hsu
Artificial Intelligence
How Well Do LLMs Reason with Noisy Evidence? An Active Visual Reasoning Benchmark
Bach Nguyen, Zhaonan Li, Mau Son Nguyen et al. (+5 more)
Real-world
reasoning rarely reduces to static question answering:
agents must actively gather information from tools and sensors that are often noisy and unreliable. Yet most existing active
reasoning benchmarks assume that environmental feedback is trustworthy, or introduce noise without exposing an explicit, calibrated uncertainty signal, leaving open how LLMs should reason when the evidence itself is uncertain. We introduce VisualNoiseQA, a novel
benchmark for active
reasoning under noisy visual feedback. A text-only LLM must solve VQA problems by iteratively querying a fixed, off-the-shelf VLM treated as a
stochastic visual sensor. For each query, we draw multiple samples and expose an empirical uncertainty signal via self-consistency, enabling the reasoner to probe from different angles and decide what to ask next and when to stop. Our construction is automatic and scalable: starting from diverse VQA sources and two noisy VLMs, we retain only questions where the sensor is inconsistent yet human-solvable. We evaluate multiple LLM reasoners on 1,000 instances spanning perception, chart understanding, and knowledge-intensive
reasoning. VisualNoiseQA thus provides a controlled playground to study how different LLMs exploit uncertainty signals for robust
reasoning.
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All authors Bach Nguyen, Zhaonan Li, Mau Son Nguyen, Sanika Chavan, Nilay Kumar, Hong Anh Nguyen, Khoa Vo, Ben Zhou
Artificial Intelligence · Cross-listed from cs.PL
Cleave: Scaling Tensor Program Optimization via Decoupled Algebraic Search and Operator Scheduling
David Pissarra, Jinkun Lin, Haitian Jiang et al. (+2 more)
Optimized kernels such as FlashAttention and FlashDecoding are crucial for accelerating today's large models. Most of them are handwritten by experts because existing ML compilers cannot match their efficiency. Producing such kernels requires fusing computations with multiple reductions, which requires both algebraic transformation of the computation graph and operator scheduling of the transformed graph. Unfortunately, searching the two jointly yields a space too large to navigate. We propose Cleave, an ML compiler built on symbolic decoupling: Cleave discovers transformations by performing superoptimization on a graph with symbolic shapes, and then schedules each resulting graph on concrete shapes. Representing shapes as symbols makes equivalence checking cheap and lets a new Split operator, with a symbolic split count, parallelize along a reduction dimension. Cleave's scheduler fuses graphs with multiple reductions through iterative tiling and horizontal fusion. Evaluation on common LLM subgraphs shows that Cleave generates kernels up to 2.8x faster than the best baseline (1.6x on average) and reduces compilation time by 5.9x on average compared to Mirage. For dynamic workloads captured from production serving traces, Cleave compiles each operator once and achieves
geometric mean speedups of 1.4x and 1.7x over FlashInfer's handwritten FA2 and FA3 backends. Cleave's code is available at: https://github.com/nyu-systems/cleave
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All authors David Pissarra, Jinkun Lin, Haitian Jiang, Aurojit Panda, Jinyang Li
Artificial Intelligence · Cross-listed from cs.IR
Learning to Retrieve via Reinforcement Learning in Embedding Space
Qi Liu, Fengming Liang, Yiqun Chen et al. (+2 more)
Dense
retrieval models are typically trained with contrastive objectives that learn effective representations but do not directly optimize
retrieval metrics or downstream task performance. To address this problem, we introduce RELER (REinforcement LEarning for
Retrieval), a reinforcement learning framework that enables existing
embedding models to learn to retrieve directly in
embedding space and align to task-specific rewards. We train RELER by sampling unit-length query and document
embedding actions from von Mises-Fisher (vMF) distributions centered on normalized encoder outputs, scoring the resulting
retrieval or downstream outcomes as rewards, and updating the encoder with REINFORCE using a leave-one-out baseline (RLOO). As exploration in the high-dimensional
embedding space is prone to sampling noise, we further propose conditional-mean projection (CMP), which projects each sampled
embedding onto the low-dimensional subspace spanned by its encoder output and the candidate
embeddings it is compared against, reducing noise in the policy gradient while preserving its expectation. We evaluate RELER on BRIGHT, a
benchmark with
reasoning-intensive queries that remain challenging for existing
embedding models. RELER consistently outperforms InfoNCE and LambdaLoss in average nDCG@10 when post-training BGE-M3 and Qwen3-
Embedding backbones. We further evaluate downstream utility through
retrieval-augmented generation (RAG), where we adapt only the query encoder while keeping the document index and generator fixed. Across seven QA
datasets, jointly optimizing
retrieval and answer rewards improves both average
retrieval performance and answer quality in RAG.
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All authors Qi Liu, Fengming Liang, Yiqun Chen, Erhan Zhang, Jiaxin Mao
Artificial Intelligence · Cross-listed from cs.CL
SanSi: A Looped Typed Decision Model for System 1.5 Thinking
Shuyu Gan, Young-Jun Lee and Dongyeop Kang
Typed decision models answer a declared question without generating text: a decision head returns a probability for each of the declared options in a single forward pass. A single pass is fast, intuitive System 1 thinking. We study what lies between one pass and generated
reasoning: looping, in which the same layers are recursively applied several times before one typed readout. Each loop lets the model revise its hidden state before it commits to an answer, without generating a
token; we call this System 1.5 thinking. We propose SanSi, which turns a pre-trained looped
language model into a typed decision model. The option probabilities are read after every loop, and every loop is trained with a proper scoring rule, so that one model serves every budget from one loop to eight in a single run. On 10,027 test decisions from 59 sources, SanSi reaches 72.0% accuracy: 13.5 points above a non-looped model of the same shape trained with the same recipe, 5.3 points above a newer non-looped model of its size, and 1.8 points below one with three times the parameters. On two depth-controlled tasks, loops extend the solvable depth beyond the depths seen in training, where the larger single-pass model fails. Used as the judge for policy optimization with reinforcement learning, without gold answers, SanSi raises the share of the generator's correct answers from 37% to 52%.
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Artificial Intelligence
PERSIST: Who-What-When Memory Across Sessions for Full-Duplex Spoken Dialogue
Achira Lin, Siyuan Hou, Wenyi Yu et al. (+7 more)
Modern voice assistants may be shared by multiple users and should be able to answer questions about earlier conversations such as "When did I originally plan to leave?" or adapt their behavior to individual users based on past interactions. This requires more than retrieving a topically similar passage: the assistant must identify the current speaker, recover the relevant past state, and distinguish it from later revisions. We present PERSIST, a persistent
memory system for multi-session, multi-speaker spoken dialogue that explicitly models Who, What, and When. PERSIST structures cross-session histories into readable event records and retrieves them with a 3W joint scoring mechanism that combines semantic content, acoustic speaker identity, and temporal state. For real-time full-duplex interaction, PERSIST further reuses intermediate representations from the dialogue backbone, avoiding query-audio re-encoding and reducing
retrieval latency from 578.42 ms to 7.03 ms. We also introduce SpokenTrace, a diagnostic
benchmark that factorizes evaluation along
memory tasks and speaker-query types, exposing failures in recall, speaker attribution, and temporal-state tracking. On SpokenTrace, PERSIST achieves 85.08% end-to-end task accuracy and improves all-support EM@3 from 49.01% with BGE-large to 82.10%.
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All authors Achira Lin, Siyuan Hou, Wenyi Yu, Xinnian Zhao, Haoyu Niu, Wang Geng, Longshuai Xiao, Shihai Xiao, Mangsuo Zhao, Chao Zhang
Artificial Intelligence
Evidence Before Sampling: Interpretable Implicit Negative Candidate Discovery for Recommendation
Shreya Rajpal, Sonia Sharma, Swapnil Parekh et al. (+4 more)
Recommender systems learn from observed user-item interactions, but explicit negative feedback is often unavailable. Since deep learning models require negative signals for training, negative sampling methods typically treat selected unobserved interactions as negatives. However, a missing interaction does not explain why a user is uninterested in an item or whether there is sufficient evidence to label it negative. This is especially important in business recommendation, where negative signals should be interpretable and aligned with business objectives. We formulate implicit negative candidate discovery to identify unobserved interactions supported by observed customer behavior. We encode these patterns as symbolic rules, score them based on support, informativeness, and product relevance, and rank the retained rules by evidence. An LLM then interprets the retained rules using business objectives and domain knowledge; the interpretations are combined with the statistical evidence in the final report. We evaluate our method in an industrial B2B setting and across five public recommendation
datasets. Candidate-quality evaluations in the industrial setting and three public
datasets show higher precision than the evaluated baselines, while symbolic selection improves downstream test PR-AUC by 12.5% over random selection with four negatives per positive example in the industrial task. Our results show that negative candidate validity can be evaluated separately from downstream recommendation performance. This distinction enables evidence-based, business-aligned, and explainable negative selection, improving both interpretability and model training in sparse, skewed, real-world recommendation settings.
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All authors Shreya Rajpal, Sonia Sharma, Swapnil Parekh, Lisa Li, Jeyendran Balakrishnan, Nagaraj Janardhana, Andrew Mattarella-Micke
Artificial Intelligence
AgentMemGate: Addressing Speculation Contamination in Conversational Assistant Memory
Chirag Sharma, Benjamin Fowlersmith and Karime Maamari
Conversational AI assistants with long-term
memory extract facts from user messages into a store consulted in later conversations. A stated plan can enter that store as fact: a user who might move to Seattle may be recorded as already living there. We call this speculation contamination. Final-state
memory benchmarks miss this error because they do not probe intermediate state and include few unresolved speculations. We present AgentMemGate, a write-time gate for profile-store
memory that classifies extracted statements as speculation, completed event, correction, or other. Speculations remain outside
memory, with conditions governing later promotion or deletion. We also contribute a
dataset of multi-session conversations in which plans are confirmed, abandoned, or left unresolved. On our 147-conversation held-out set, Mem0 and Graphiti assert unresolved plans as current state for 35.2% and 27.3% of pending plans. On the core
benchmark, AgentMemGate eliminates all observed contamination relative to the identical ungated pipeline (87.5% to zero for the most exposed extraction style) and raises task accuracy from 65% to 95%. On the harder held-out set, gated contamination is 3.4% to 5.7% and task accuracy rises by 9 to 13 percentage points. Our analysis identifies field matching as the main remaining bottleneck: realistic speculations often match no profile field and never reach the gate. We release our
datasets,
prompts, and evaluation code.
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Artificial Intelligence · Cross-listed from cs.LG
WASD: Wasserstein-based Knowledge Distillation for Large Language Models
Byeonghu Na, Donghyeok Shin, Yeongmin Kim et al. (+2 more)
Autoregressive large
language models (LLMs) have rapidly advanced in capability, but their increasing scale comes with substantial computational and
memory costs at inference time. Knowledge
distillation (KD) offers a practical solution by transferring knowledge from a large teacher model to a smaller student model via
alignment of discrete probability distributions. However, existing KD methods for LLMs primarily rely on divergences that evaluate discrepancies through probability values at each vocabulary index, without explicitly leveraging
token-level semantic information. We propose Wasserstein-based knowledge
distillation (WASD) for LLMs, which incorporates
token-level semantic information via the Wasserstein-based distance with a cost
matrix derived from
token embeddings. To ensure computational tractability, we adopt the Sinkhorn divergence and derive a gradient-equivalent objective that can be efficiently optimized without introducing additional networks. Experiments across multiple LLM families and scales show that WASD consistently improves
distillation performance on diverse tasks, including instruction following, mathematical
reasoning, and code generation. Our results highlight the importance of semantic information encoded in the
token space for effective distribution
alignment in LLM
distillation. The implementation is publicly available at https://github.com/aailab-kaist/WASD .
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All authors Byeonghu Na, Donghyeok Shin, Yeongmin Kim, Mina Kang, Il-Chul Moon
Artificial Intelligence · Cross-listed from cs.CV
What Frame-Level Labels Can and Cannot Do for Small-UAV Point Detection in Thermal Video
Wonbin Son, Gyumum Choi, Junil Seo et al. (+1 more)
The growing use of unmanned aerial vehicles (
UAVs) has increased the importance of image-based
UAV detection. Learning-based detectors are trained on imagery and annotations, with annotation type determining the information available during training. We focus on learning localization from frame-level target presence/absence labels when sensor or scene changes make spatial annotations for additional training burdensome. We analyze the detection capability, learning behavior, and potential applications of an existing architecture for point detection of small
UAVs, trained with presence/absence labels and requiring no external detector. The architecture freezes spatial features learned through classification and trains a readout with the same frame labels to produce spatial score maps and point detections. On two thermal infrared
datasets, CST Anti-
UAV and Anti-
UAV410, we evaluate localization hit rates and detection rates under false-alarm constraints, analyze the effects of training stages, label allocation, synthesis, and model configuration, and compare with bounding-box detectors. We also explore potential applications on Airborne Object Tracking (AOT) using its visible-light imagery and frame labels. Classification training strengthened target-related spatial responses, while readout training helped extract them consistently. Distributing similar label counts across more
videos yielded higher localization hit rates, while synthesis effects varied by
dataset and evaluation criterion. Higher localization hit rates did not always improve detection under false-alarm constraints, and failures remained when target signals were weak relative to background variation and under cross-
dataset transfer. These findings provide guidance on label allocation, spatial representations and readouts, synthesis, and false-alarm control.
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All authors Wonbin Son, Gyumum Choi, Junil Seo, Hyungjoon Kim
Artificial Intelligence
On the Boundary of Admission Gates: An Injected-Truth Study of Falsification-First Selection in Quantitative Strategy Research
Tianlun Zheng
Strategy research conflates two problems: finding a profitable rule, and establishing that the finding is not search luck. The latter calls for admission gates – statistical criteria that must be satisfied before a conclusion is adopted – yet whether gates work, and at what cost, remains untested. We introduce an injected-truth protocol with a random-admission control that adopts at the same rate as the gate; only if the gate beats this control does it carry information rather than merely raise a threshold. Across synthetic and real-calibrated panels, gates eliminate false discoveries in the weak-signal regime but cut adoption to 1–7%, and add nothing when signals are strong. Most importantly, criteria computed on absolute rather than excess returns silently reject every candidate, including true signals. Keywords: multiple testing, backtest overfitting, strategy admission, injected-truth validation, excess returns, false discovery rate
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