Artificial Intelligence
Adapting to Evolving Requirements: Agentic AI for Retail Supply Chain Operations
Lei Zheng, Liping Yang, Zihao Li et al. (+3 more)
Retail supply chain operations rely on coupled decision modules that must adapt as requirements evolve. LLMs offer a natural-
language interface for this task, but existing methods primarily focus on individual optimization models. Extending them to heterogeneous decision pipelines is challenging because a requirement may admit multiple intervention paths with different downstream effects. We formulate requirement-driven adaptation as the joint selection of an intervention route and an admissible module-level change, and propose a graph-constrained agentic framework in which domain
agents expose admissible reformulation interfaces and a central processor searches over bounded intervention paths. Candidates are validated and compared using downstream KPIs. In collaboration with a large retail partner, we evaluate 100 warehouse requirements elicited from practitioner interviews, with GPT, Qwen, and DeepSeek as base LLMs. Relative to direct LLM reformulation, our framework improves correctness and end-to-end success across all three models, raising end-to-end success from 72–76% to 79–83%.
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All authors Lei Zheng, Liping Yang, Zihao Li, Guodong Lyu, Chaik Ming Koh, Chung-Piaw Teo
Artificial Intelligence
Semantic Bayesian World Models
Tommaso Soru
Knowledge graphs describe reality in crisp assertions, while the systems now consuming them, foundation models and autonomous
agents, reason natively in probabilities. We argue that this mismatch is why the integration of
language models and knowledge graphs remains a data-feeding pipeline rather than a unified
reasoning architecture. We envision Semantic
Bayesian World Models (SBWMs): a Web that describes the world not as a database of facts but as a shared, evolving fabric of beliefs over knowledge graphs, where ontological axioms constrain priors, observations update beliefs by
Bayesian conditioning, and actions intervene upon the world. We work through what an
agent gains from such a model: a home-security
agent deciding whether the figure at the gate is a courier or a burglar, an actuarial estimate aggregated by entailment rather than by string frequency, a planning task that
language models reliably fail, and the estimation of quantities that no document has ever stated. We then set out what the community must build to make them possible: belief annotation over RDF~1.2, probabilistic entailment regimes, semantic calibration layers, and protocols by which
agents that have never met can exchange, and disagree over, calibrated beliefs.
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Artificial Intelligence · Cross-listed from cs.CV
Phase-Aware Spatial-Frequency Fusion for Few-Shot Fine-Grained Image Classification
Ruiling Liu, Linyue Zhang, Wenyi Zeng et al. (+5 more)
Few-shot fine-grained image classification (FSFGIC) aims to classify similar images with limited labeled examples. This work highlights the critical yet underutilized role of phase information in capturing structural relationships within an image. This study introduces a novel plug-and-play amplitude-phase integration (API) module that effectively combines local and global frequency amplitude and phase information for obtaining more comprehensive feature descriptors. Additionally, a dedicated network, named PSF-Net, is proposed that adaptively fuses phase-based spatial and frequency information for FSFGIS. The designed PSF-Net can be easily integrated into standard episodic training architectures for end-to-end training from scratch. Extensive experiments on five public
datasets demonstrate that the method outperforms existing state-of-the-art
benchmarks.
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All authors Ruiling Liu, Linyue Zhang, Wenyi Zeng, Jiamiao Lu, Weichuang Zhang, Changming Sun, Zejun Zhang, Xiao Zhao
Artificial Intelligence · Cross-listed from cs.LG
Witnesses Explain Anomalies
Lamine Diop
Unsupervised anomaly detection scores each point of an unlabelled, contaminated sample in a single pass, and increasingly must also explain why a point is flagged. Yet the dominant detectors give a score with no account of which features drive it, and explanations are bolted on post-hoc with SHAP or LIME, which re-query the detector thousands of times per point and only approximate it. We introduce WAND, an unsupervised tabular anomaly detector that is explainable by design. WAND organises its computation around directions on the unit sphere, scoring each point by how far its projection escapes a sub-Gaussian extreme-value baseline. The originality of our approach is that the witness directions that flag a point, being vectors in feature space, are its explanation, a per-feature attribution obtained at no cost over scoring and, since the score is differentiable, recoverable by gradients. Scoring is linear in the sample size, and a probe-efficiency bound guarantees every anomaly a witness, hence an explanation. Across 47 ADBench
datasets WAND attains the best mean Friedman rank at ROC-AUC parity with 16 unsupervised baselines, so the gain is interpretability at no accuracy cost; its native explanations are more accurate and faithful than post-hoc SHAP/LIME and ECOD at a fraction of the query cost. WAND is thus a practical, interpretable solution for explainable anomaly detection.
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Artificial Intelligence
CauseCollab: Causal Unified and Modality-Agnostic Network for Heterogeneous Collaborative Perception
Weize Li, Yang Li, Quan Yuan et al. (+3 more)
Collaborative perception enhances environment understanding through multi-
agent information sharing, but its performance in real-world scenarios is constrained by heterogeneous sensor modalities and model architectures. Recent protocol-based two-stage methods alleviate this problem by mapping heterogeneous features into a shared protocol space; however, independently trained modality-specific converters often generate modality-specific pseudo-protocol distributions, leading to semantic inconsistency and error accumulation, which is particularly pronounced in scenarios with large modality discrepancies. To address this issue, we propose CauseCollab, a causal unified and modality-agnostic network. CauseCollab formulates representation learning in the protocol space from a causal perspective, explicitly disentangling semantic factors from modality-specific statistical confounders via causal metric learning. Meanwhile, CauseCollab adopts context-guided Unified Converter for heterogeneous modalities to ensure cross-modal semantic consistency. In addition, integrating new modalities only requires training adapters with minimal parameters. Extensive experiments on the OPV2V and DAIR-V2X
datasets demonstrate that CauseCollab achieves state-of-the-art performance, with more significant gains in scenarios involving large modality gaps.
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All authors Weize Li, Yang Li, Quan Yuan, Xiaoyuan Fu, Guiyang Luo, Jinglin Li
Artificial Intelligence · Cross-listed from cs.LG
Almost Free State Prediction Separation
John Langford, Nathan Godey, Giovanni Monea et al. (+5 more)
State–prediction separation (SPS) relieves a
language model's hidden state of two competing burdens—summarizing the context and predicting the next
token—by splitting the forward pass into a state stream and a prediction stream. The separation works, but it is expensive: the prediction stream is a second pass over the whole backbone, costing $\sim$1.9$\times$ the pretraining FLOPs, and even more in terms of wall-clock time when using a flexible
attention mask. This paper makes state–prediction separation almost free. We take the separation to its limit with a free pause
token: a prediction stream that writes no keys or values at all and so rides the sequence's existing positions. It improves next-
token prediction of a standard
Transformer by 2-3 centinats in practice on a 1B parameter model, and because it adds no position it costs nothing at inference—no added context length, no KV cache, no decode steps, and essentially no latency, with the growth in inference flops typically irrelevant as it is not the active bottleneck on throughput. The cost is therefore entirely in training where we use four mechanisms to drive it down: a two-pass split that keeps FlashAttention kernels viable, the $w{=}0$ prediction window, a shared gated FFN that evaluates one FFN per position rather than one per stream, and phasing the separation onto the tail of the run. Together these bring the overhead versus an optimized pretraining pipeline to $1.33\times$ wall-clock while recovering ~94% of the gain compared to SPS, and to as low as $1.09\times$ along a graceful quality/compute tradeoff. Furthermore, the FFN optimization reduces the raw flops required at inference time. The result is an isoflop, isoparameter, and isotoken improvement over standard next
token trained
transformers.
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All authors John Langford, Nathan Godey, Giovanni Monea, Yoav Artzi, Harry Dong, Ying Fan, Gustavo de Rosa, Zheng Zhan
Artificial Intelligence
SVG-Score: Human-Aligned Evaluation of Text-to-SVG Generation
Marco Cipriano, Leonardo Zini, Alexandra Schild et al. (+5 more)
Scalable Vector Graphics (SVG) generation is attracting increasing
attention as generative models improve in expressiveness and controllability. Progress, however, is held back by the lack of domain-specific evaluation protocols: current practice relies on metrics designed for natural images, most notably CLIPScore, which was never trained on vector graphics and aligns only partially with human judgment. We introduce \ours, a human-aligned evaluation framework for text-to-SVG generation. Through controlled caption and image perturbations, we first show that CLIP-based scores barely react to the errors SVG generators actually make, such as wrong colors, counts, and spatial relations, and that off-the-shelf
Vision-
Language Model (VLM) judges, while more sensitive, respond unevenly across error types and SVG styles. We then introduce a human-annotated
dataset for Semantic
Alignment, measuring how faithfully a generated SVG reflects its caption. Building on it, we develop two complementary evaluators: CLIP scorers adapted to vector graphics and then aligned to human preferences, for fast large-scale evaluation, and a VLM judge trained with supervised fine-tuning and reward-shaped reinforcement learning, for more expressive and interpretable assessment. Using both, we
benchmark major open-source, commercial, and optimization-based SVG generators on an independent caption set.
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All authors Marco Cipriano, Leonardo Zini, Alexandra Schild, Valentin Teutschbein, Afsana Mimi, Marcella Cornia, Lorenzo Baraldi, Gerard de Melo
Artificial Intelligence
Govern the Model, Not Only the Data: Storage, Circulation, and Learning in Creative AI
Phoenix Perry, George Simms, Elizabeth Wilson et al. (+9 more)
Federated learning is increasingly presented as a
privacy-preserving advance: personal data remain on the device, and only model updates are shared. It borrows the vocabulary of the
federated social web, yet inverts its logic, distributing computation while the resulting model stays with whoever convened the training. We argue that federation is not in itself a remedy for extractive AI, because outcomes depend on who governs the data and the model and who has agency over the practices that shape them. We describe three layers at which a creative community can hold its work: storage, circulation, and learning. Examining artist-governed trusts, cooperatives, and consent infrastructures, we show that creator governance is established at storage and circulation but stops at learning: contributors can consent to training, yet have little say over the resulting model or its federation. We map the research space this opens, pairing technical open problems with the human questions from which they unfold. We propose four design principles for a creative data commons that governs models and their federation, not only
datasets: govern the model, not only the corpus; make the terms legible at the moment of contribution; design for refusal as a first-class state; and decide stewardship in the open and account for it.
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All authors Phoenix Perry, George Simms, Elizabeth Wilson, Yasmine Boudiaf, Nick Bryan-Kinns, Tega Brain, R. Luke DuBois, Alix Rule, Rachel Meade Smith, Kelani Nichole, Atharva Pravin Pawar, Rebecca Fiebrink
Artificial Intelligence
Transfiver: Human-AI Co-Inference through a Shared Editable State
Minji Park, Seunghyun Yoon and Hyuk Lim
Long-term human-AI interaction is difficult because the information that guides inference is updated implicitly by the model and is not directly inspectable or controllable by the user. We introduce the TRANSparent Framework for Interactive, Verifiable, Editable Representation (Transfiver), an architecture for human-AI co-inference through a shared editable state. Its central idea is that interaction-specific information is maintained in a single persistent state $(S_t)$ that both the model and the human update. Transfiver distinguishes two modes of state
evolution. In an implicit stream update, the model interprets ongoing interaction and decides whether new information revises an existing state item or creates a new one. In an explicit directed edit, a human inspects and modifies an addressed item. Both act on the same underlying state, so a human correction changes the state that subsequent computation reads, rather than adding another instruction or separate record. The architecture separates shared parameters $(\theta)$, learned before ordinary use, from the persistent state $(S_t)$, which evolves during deployment without parameter retraining. Extending Transfiver to rich natural-
language, relational, and large-scale shared states remains open.
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Artificial Intelligence · Cross-listed from cs.CV
LLaDA-Image: Building Strong Image Generators with Fully Open Training Recipes
Chuyan Chen, Haoxing Chen, Kun Chen et al. (+27 more)
We introduce LLaDA-Image, a unified framework that pairs a 6B
Diffusion Transformer (DiT) trained from scratch with a frozen
vision-
language understanding module built on the LLaDA2.0-Mini
diffusion language model backbone. Instead of relying heavily on paired image-text data from the beginning, we first build a strong visual generative prior through image-only pre-training and mid-training. The generation pipeline comprises 220M samples, 98 of which are real images. For efficient and scalable optimization, we use parameter-free RMSNorm throughout the DiT together with the
Muon optimizer. The resulting unified model produces highly photorealistic images while accurately following fine-grained editing instructions. We further distill LLaDA-Image into LLaDA-Image-Turbo, enabling fast inference in 2-4 sampling steps. On Qwen-Image-Bench, LLaDA-Image achieves overall scores of 53.53 and 53.38 on the English and Chinese tracks, respectively, setting a new state-of-the-art among open-source models on both tracks. To support further research on capable and efficient generative models, we release our model weights, training code, and detailed recipes.
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All authors Chuyan Chen, Haoxing Chen, Kun Chen, Zhenglin Cheng, Long Cui, Ruishan Fang, Zhangxuan Gu, Zhicheng Huang, Zhenzhong Lan, Yuanting Lei, Haoquan Li, Jianguo Li, Rongchuan Li, Sidu Li, Tao Lin, Deyuan Liu, Jiacheng Liu, Lin Liu, Yuxuan Lou, Zhisheng Lu, Yuxin Ma, Shuheng Shen, Peng Sun, Chaoyang Wang, Hongjun Wang, Xiaomei Wang, Yongxin Wang, Chengzhang Wu, Hongru Wu, Jun Xie
Artificial Intelligence
DNative-Twin: Decision Graphs and Digital Twins for Reconstructable Agentic Decisions
Junjie Pang, Zhenzhen Xie, Haoke Han et al. (+3 more)
AI
agents increasingly gather evidence, invoke tools, apply constraints, and produce decisions that people or software may commit to action. A final output alone cannot show which evidence, tool state, rule, authorization, or action path produced it. We present DNative-Twin, a graph-native digital twin that records a committed agentic decision as a typed trajectory and re-executes its decision mechanism under declared conditions. The graph links the state observed by the
agent, the path it followed, and the authority behind the resulting action. The twin synchronizes this information, replays the mechanism in isolation, and compares it under controlled changes. We instantiate the framework in enterprise decision processes using three public process logs and controlled replay suites. The experiments identify a specific failure: graph structure localizes represented changes but cannot determine the consequence of an unobserved tool state. In a three-condition controlled experiment with 300 injected instances, unresolved-divergence recall increased from 0 to 0.667 when replay-contract state was added and to 1.0 when verification results were also available; the held-out set contained no critical-class instance. Across 500–5,000 BPI 2020 cases, median end-to-end time increased from 0.794 to 8.889 seconds on the reported platform. These results separate the roles of graph structure, replay context, and verification evidence in reviewing a decision mechanism.
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All authors Junjie Pang, Zhenzhen Xie, Haoke Han, Ying He, Jing Wang, Gang Liu
Artificial Intelligence · Cross-listed from cs.CL
IndicSafeEval: Safety Robustness of Large Language Models under Multilingual Persuasive Jailbreak Attacks
Saikat Mondal, Mamta, Deeksha Varshney et al. (+2 more)
Large
language models (LLMs) are increasingly used in
multilingual settings, yet their safety is still evaluated primarily in English. This limits our understanding of how
alignment failures manifest in low-resource and culturally diverse
languages. We introduce IndicSafeEval, a persuasion-based jailbreak evaluation framework for Indian
languages. Our
benchmark combines ten safety critical content categories with six human-like persuasive strategies across four different Indian
languages, such as Hindi, Bengali, Marathi and Punjabi, resulting in 7,200
adversarial prompts. We conduct a systematic black-box evaluation of several open-source LLMs to examine how their safety behaviour varies across
languages, persuasion strategies, and risk categories. Our analysis shows that the model does not behave equally safely across all
languages and
prompt styles. Instead, safety performance depends strongly on both the
languages used and the way a request is phrased using persuasive cues. We further observe that different risk categories exhibit different levels of vulnerability, with some types of harmful content being significantly more susceptible to persuasion-based jailbreaks than others. These findings reveal important limitations of current safety evaluations, which are largely English-centric, and underscore the need for
multilingual and persuasion-aware benchmarking frameworks to more accurately assess real-world LLM safety. Our implementation is available at https://github.com/MonSaikat/IndicSafeEval. Warning: this paper contains example data that may be offensive or harmful.
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All authors Saikat Mondal, Mamta, Deeksha Varshney, Oana Cocarascu, Asif Ekbal
Artificial Intelligence
Rethinking World Models for Safety-Critical Embodied Systems
Kailang Ma, Heye Huang, Inhi Kim et al. (+1 more)
World models have progressed from compact latent dynamics to generative, controllable, and interactive simulators of
embodied environments. However, high predictive likelihood and visual fidelity do not necessarily ensure that a model preserves the evidence required for safe decision-making. This perspective identifies three structural mismatches in current world modeling: likelihood versus risk, prediction versus intervention, and finite-horizon prediction versus accumulated consequences. We propose the Risk-Informed World Model (RIWM) as a decision-centric research direction for safety-critical
embodied systems. RIWM organizes world modeling around consequences, intervention, epistemic uncertainty, and recoverability, and integrates four interdependent capabilities: decision-relevant representation, counterfactual
reasoning, safety-critical episodic
memory, and runtime safety assurance. It distinguishes physical, social, and operational consequences while using epistemic uncertainty to qualify the evidence supporting action. We further discuss open challenges in identifying consequential futures, validating counterfactual
reasoning, maintaining revisable safety
memories, translating learned consequences into executable constraints, and determining when evidence is sufficient to act. This perspective argues that future world models should move beyond predicting likely futures toward identifying which futures matter, revising judgments through experience, and recognizing when to act, revise, sense, defer, or abstain.
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All authors Kailang Ma, Heye Huang, Inhi Kim, Kitae Jang
Artificial Intelligence · Cross-listed from cs.CV
ENEAS: Embedding-guided Neural Ensemble for Adaptive Segmentation
Javier del Pino, Salvador Rodríguez, Alejandro Garabito et al. (+2 more)
We present ENEAS, a unified, text-promptable method for instance tracking and semantic discovery. Text-promptable
segmentation models, including the latest foundation models such as SAM 3, still suffer from temporal
hallucinations, spatial fragmentation, and semantic misclassification: they fail to report target absence when an object leaves the field of view, segment local textures instead of the complete object during extreme close-ups, and prioritize visual features over ontological reality, so that visually similar artifacts such as statues, paintings, or reflections are segmented as target entities. ENEAS works two ways from a single method: precise tracking and high-quality
segmentation of a unique instance, and open-concept discovery of every instance a text query names, resolved by a semantic verification layer. For tracking, we extend the geometrically robust SeC architecture, previously limited to point interactions, with a text-prompting adapter and leverage its temporal
memory, so that the target is held through disappearance without drifting to distractors and kept whole even when it fills the entire view. For discovery, the verification layer combines high-speed visual
embedding matching with conditional VLM refinement, invoking semantic
reasoning only for ambiguous candidates, which filters out the ontological errors that visual-only models cannot distinguish while keeping latency low. Designed with 3D reconstruction in mind, where a single misclassified distractor corrupts the asset, ENEAS unlocks high-quality semantic tracking and
segmentation of
video, of broad libraries, and of collections of temporally or spatially unordered data, together with the discrimination to tell true instances from their doppelgangers: things that look alike but are not the same. The code and models are available at https://github.com/speridlabs/eneas
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All authors Javier del Pino, Salvador Rodríguez, Alejandro Garabito, Javier Álvarez, Chema Garabito
Artificial Intelligence
SimSkill: A Self-Evolving LLM Agent for Skill and Knowledge Accumulation in Traffic Simulation
Qi Liu, Qinzheng Wang, Can Li et al. (+2 more)
Cumulative culture enables humans to preserve, reuse, and extend knowledge and skills across experiences and generations. Inspired by this principle, we introduce SimSkill, a self-evolving
agent built around the Simulation of Urban MObility (SUMO) traffic simulator. SimSkill continually identifies capability gaps, generates and solves environment-grounded tasks, verifies solutions through an action–critic loop, and consolidates experience into episodic, procedural, and semantic
memory. Through autonomous exploration, it builds a library of reusable skills and knowledge spanning major stages of the traffic-simulation workflow. We evaluate SimSkill on two held-out
benchmarks across three backbone LLMs, with each result independently verified. It improves verified success by up to 25 percentage points, and ablations show complementary contributions from procedural and semantic
memory. Its benefits remain backbone- and budget-dependent, as
memory does not improve every model or uniformly reduce inference cost. More broadly, SimSkill illustrates a natural-
language-centered design paradigm for LLM-based
agent systems. Its high-level control logic, operating principles, and accumulated knowledge are expressed in natural
language, while an LLM integrates them with executable tools and code to realize precise and reproducible execution. All code and experimental data are publicly available at https://github.com/qiliuchn/SimSkill-V1.
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All authors Qi Liu, Qinzheng Wang, Can Li, Yiming Bie, Wanjing Ma
Artificial Intelligence · Cross-listed from cs.CL
Beyond BLEU: A Case for Redefining Sign Language Translation Benchmarks
Oline Ranum, Edward Fish, Simon Hadfield et al. (+1 more)
BLEU-4 is the standard metric for evaluating sign
language translation (SLT), but spoken-
language metrics may not adequately reflect sign
language proficiency. The
multimodal, low-resource context of SLT allows models to exploit spurious correlations and spoken-
language priors, rather than learning stronger sign representations. In this paper, we evaluate the relationship between spatio-temporal understanding and BLEU-4 across six SLT models on Phoenix-2014T and CSL-Daily, showing that gains in BLEU-4 are not on their own evidence of better sign
language understanding. This work introduces an alternative inspired by
language-learning assessment, using an open-weight-LLM QA protocol that measures salient content preservation. It aligns more closely with human rankings and is six to seven times more paraphrase-invariant than BLEU-4. Applied to SLT, this protocol targets content transfer, is more robust to train-test overlap, and gives a different picture of the field: the five gloss-free systems are largely within noise of one another on Phoenix-2014T, while the gloss-supervised system stands 9.3 points higher, a gap invisible to BLEU-4.
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All authors Oline Ranum, Edward Fish, Simon Hadfield, Richard Bowden
Artificial Intelligence
Proactive Service Agents: A Unified Decision Framework, Methods, and Evaluation
Yan Tang, Tingyu Cao, Yuanbo Tang et al. (+2 more)
Large
language model
agents can plan, invoke tools, and modify external states, yet most systems still take an explicit user instruction as a fixed starting point. Proactive service moves the decision upstream: an
agent must infer service opportunities from incomplete environmental and user signals, choose among remaining silent, asking, assisting, and acting, and account for interruption, misunderstanding, overreach, and
privacy costs. This survey gives an operational definition centered on initiative and formulates the problem as a partially observable sequential decision process constrained by authorization and risk. The formulation represents timing, content, and delivery within one structured action, while making explicit the option value of waiting, the decision value of questions, and feedback-induced state changes. On this basis, we organize existing methods along one decision pipeline (state and need estimation, intervention gating, action construction, and feedback adaptation) and describe prescribed, predictive, model based, and return optimizing mechanisms as nonexclusive policy-construction components. We further normalize decision units and three-axis evidence descriptors across streaming dialogue, screen,
video, software-engineering, and human-
agent collaboration resources, and formalize metrics for triggering, timing, calibration, user burden, safety, and policy value. The synthesis shows why offline classification performance alone does not predict deployment benefit and why long-term
memory is not a defining condition of proactivity. Reliable proactive service instead requires calibrated incremental intervention value, verifiable authorization, recoverable execution, and counterfactual evidence.
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All authors Yan Tang, Tingyu Cao, Yuanbo Tang, Huaze Tang, Keer Hu
Artificial Intelligence · Cross-listed from cs.SE
Can LLMs Extract Architectural Design Decisions from Source Code Commits? - A Preliminary Exploratory Study
Amey Karan, Rudra Dhar, Mohamed Soliman et al. (+1 more)
Context: Architectural Design Decisions (ADDs) capture the rationale behind the structure and
evolution of software systems but are rarely documented explicitly, and are often hidden inside source code commits. Recovering them is important for Architectural Knowledge Management (AKM). Problem: Extracting ADDs from commits is challenging due to their implicit and unstructured nature. Large
Language Models (LLMs) have shown strong capabilities in understanding code and text, yet their effectiveness for this task remains underexplored. Study: We present a preliminary study using four LLMs (Gemini 3 Pro, DeepSeek R1, Kimi K2, Qwen3) with zeroshot and fewshot prompting on 30 developer-written ADDs from open-source projects. We score outputs with ROUGE-L, BLEU, METEOR, and BERTScore, and one author manually reviews the Gemini outputs. Results: All models reach a BERT-F1 above 0.81, and fewshot prompting improves
alignment (Gemini BERT-F1: 0.828 to 0.847). However, the generated ADDs are often too long, implementation-focused, and miss the rationale behind the decision. This highlights opportunities for architecture-aware LLM systems and automated AKM.
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All authors Amey Karan, Rudra Dhar, Mohamed Soliman, Karthik Vaidhyanathan
Artificial Intelligence
Artificial Intelligence for Energy Optimization in Data Centers
Mohammed Basharath Ullah, Summaiya Unnisa Begum and Mohammed Nadeem Ullah
Data centers are increasingly optimized by artificial intelligence and, at the same time, increasingly loaded by it. The literature treats these as two unrelated problems: control studies model workload as an exogenous arrival process, while sustainability studies model infrastructure as a fixed multiplier. We screen roughly 194 papers retrieved through a documented protocol, code 63 of them, and report what the coding shows. Of 28 primary control-oriented studies, 18 are validated in simulation alone and 5 reach physical hardware or a production facility; none account for water withdrawal, and none account for
embodied carbon. Reported savings intervals across four technique families overlap almost completely, which means the field cannot presently rank its own methods. Ten recurring gaps are scored for consequence and tractability, and we set out CLEAR-DC, a framework coupling a control-policy branch to a workload-demand branch through an explicit elasticity term, reads out net rather than direct benefit, and emits a schema-conformant record covering energy, carbon, water,
embodied share and validation venue. The framework is an architectural and methodological proposal, not a trained system; the contribution we defend empirically is the corpus analysis and the reporting schema derived from it. Coding sheet, derived statistics and all result artifacts: https://github.com/Kimalice/AI-for-Energy-Optimization-in-Data-Centers-Closing-the-Optimizer-Load-Loop
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Artificial Intelligence
Counterfactual Routing Using Integer Programming with Constraint Generation
Daniël Vos and Sterre Lutz
We present our submission to the IJCAI 2025 'Counterfactual Routing Competition' (CRC 25). The goal of the competition is to find counterfactual explanations for the shortest path problem. This requires deciding what the minimal changes to a road network would make a route chosen by the user the optimal route. This enables explanations such as "Your suggested route would indeed have been optimal, if road X were not a bicycle path." Our solution models the problem as an integer program, iteratively incorporating constraints until an exact solution is found. In the final evaluation on held-out test instances, our method ranked fourth in solution quality and obtained its solution fastest on every instance, with an average runtime of 9.0 seconds compared to 118.8 seconds for the next-fastest submission.
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Artificial Intelligence
Synthetic Semantic Supervision for Contrastive Code Representation Learning in Small Transformers: An Empirical Study
Kenneth Paulsen, Florian Tambon, Mike Papadakis et al. (+1 more)
General-purpose code
embeddings power tools for code search, classification, and
retrieval. Compact
transformer encoders for code typically rely on either human-written docstrings (labor-intensive and inconsistent) or mined structural signals such as execution traces (setting-specific and costly to collect). We empirically study an alternative: contrastive pretraining of small encoders with synthetically generated natural-
language descriptions emphasizing code functionality and intent, paired with code in a dual-encoder framework at training and discarded at inference. We
benchmark this approach against pretraining-based baselines, generalist LLMs, and
embedding-specific models on eight
retrieval, classification, and generation tasks across C, C++, and Java. Synthetic semantic supervision yields statistically significant gains over pretraining baselines of the same inference-time size on five of eight tasks, with parity on two more; once fine-tuned, it matches or exceeds zero-shot models two orders of magnitude larger on classification, and it stays on par with execution-aware supervision at matched pretraining data, suggesting a scalable, effective alternative to existing code-representation paradigms.
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All authors Kenneth Paulsen, Florian Tambon, Mike Papadakis, Shin Yoo
Artificial Intelligence · Cross-listed from math.ST
Symmetries and Causality: Causal Effect Identification Beyond IID Data
Martin Rabel and Jakob Runge
We develop theory for the identification of complex causal queries from non-IID data. We propose a formal description of
stochastic systems based on
symmetries in data leaving causal mechanisms invariant. These
symmetries become the source of statistical repetition for identification and mandate knowledge-transfer from observations to queries. The result is an abstract, simple and very general
language for causal
reasoning. We give descriptions of models and queries, and the formal infrastructure for their mathematically rigorous identification. The proposed structure enables a number of simplifying principles: A shared description of mechanisms and interventions, a duality of observed and queried structure, and the sub-division of the identification task into three steps - extraction, computation and query-expression. Our approach reproduces and matches standard theoretical results on IID data and transport of experimental and non-experimental data. By design, it additionally applies beyond IID data and captures complex causal queries not expressible via do- or soft-interventions.
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Artificial Intelligence · Cross-listed from cs.CR
AlcaTRAz - Anchored Tree-Rule Defense Against Jailbreaks
Jakub Reš, Petr Kaška, Martin Perešíni et al. (+2 more)
Large
language models (LLMs) are vulnerable to jailbreak attacks that bypass safety
alignment through carefully crafted
prompts. Many existing defenses require access to model weights or internals, making them difficult to apply to black-box deployments. We propose AlcaTRAz (Anchored Tree-Rule defense Against jailbreaks), a
prompt-level defense based on rule trees that operates exclusively on the input text and requires no modification or retraining of the target model. The method automatically learns a transferable transformation rule that inserts controlled character-level perturbations at selected positions, thereby disrupting structural regularities exploited by jailbreak attacks while largely preserving the model's utility on benign queries. We evaluate the proposed method across 33 open-weight models, 22 jailbreak attack types, and a
benchmark of short, single-turn benign questions, comparing against three representative
prompt-level baselines (Llama Guard, RA-LLM, Goal Prioritization). Among the compared defenses, AlcaTRAz achieves the best composite security and functionality score in 73.4 % of model-attack combinations and shifts the aggregate score from a modal value of 10 (maximal-severity response to the malicious request) in the undefended setting to a modal value of 2 (near-refusal) after defense, while keeping the mean benign score within 0.27 points of the undefended baseline (8.35 vs. 8.62 on a 0-10 scale). AlcaTRAz substantially reduces but does not eliminate jailbreak success: a high-severity tail remains, and we do not consider adaptive attackers, so we position it as one layer within a defense-in-depth strategy rather than a standalone guarantee.
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All authors Jakub Reš, Petr Kaška, Martin Perešíni, Martin Ukrop, Kamil Malinka
Artificial Intelligence · Cross-listed from cs.LG
Out-of-Distribution Generalisation with Sequence Models in Offline Multi-Agent Reinforcement Learning
Oussama Hidaoui, Omer Ebead, Ulrich Armel Mbou Sob et al. (+14 more)
Generalising to unseen tasks remains a fundamental challenge in offline multi-
agent reinforcement learning (MARL). In this work, we present a principled analysis of zero-shot task generalisation in the offline setting and conduct an extensive empirical investigation into the scaling behaviour governing task diversity,
dataset size, and network capacity. To facilitate this study, we extend offline sequence modelling architectures to handle multi-task observation and action spaces alongside variable
agent counts across tasks. Our primary finding is that scaling task diversity—rather than sheer
dataset size is the dominant factor in achieving robust zero-shot transfer. Through large-scale experiments across four challenging environments (Connector, RWARE, SMAX, and LBF), we demonstrate that our multi-task approach achieves a mean improvement of 3.2x on held-out test tasks compared to single-task models and consistently outperforms strong behaviour cloning baselines. These results suggest that the development of generalisable MARL
agents should prioritise the diversity of the training distribution with varying numbers of
agents, providing a roadmap for scaling offline MARL effectively.
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All authors Oussama Hidaoui, Omer Ebead, Ulrich Armel Mbou Sob, Siddarth Singh, Juan Claude Formanek, Felix Chalumeau, Omayma Mahjoub, Sasha Abramowitz, Ruan John de Kock, Wiem Khlifi, Louay Ben Nessir, Simon Verster Du Toit, Daniel Rajaonarivonivelomanantsoa, Asim Awad Osman, Arnol Manuel Fokam, Refiloe Shabe, Arnu Pretorius
Artificial Intelligence · Cross-listed from cs.CV
Cross-Dataset Transfer and Reliability of Explainable Artificial Intelligence for RhythmFormer Remote Photoplethysmography
Louis Chen and Torbjörn E. M. Nordling
Background. Remote photoplethysmography estimates the cardiovascular pulse from facial
video, and its explanations have rested on inspecting heatmaps rather than on quantitative evidence about where a model reads it. We quantified the explanations and asked whether such explanations transfer between
datasets and track model performance. Method. We trained eight condition-specific RhythmFormer models on NCKU-rPPG, recorded under three illumination levels, speaking, rotation, and cycling, estimated one heart rate per 5.12-second clip, and set them beside a UBFC-rPPG reproduction. Raw
attention, rollout,
attention flow, and Beyond Intuition were assessed by skin coverage and the Salience-guided Faithfulness Coefficient (SaCo). Results. Beyond Intuition ranked highest on both
datasets, at median coverage 0.789 and SaCo 0.837 on Static level 3 against 0.826 and 0.917 on UBFC-rPPG; lower ranks differed. Within one participant of one condition, neither measure was related to a clip's heart-rate error, waveform correlation, or signal-to-noise ratio on either
dataset: 186 of the 252 coefficients fell below $|\rho|=0.10$ and 28 reached $p<0.05$ against the 13 expected by chance. Across the eight scenarios only Beyond Intuition's coverage followed the three performance measures, at $\rho=-0.43$, $+0.57$, and $+0.43$, while the
attention-only methods' SaCo ran opposite to each. It failed at 40 lux alone, its median coverage falling to 0.180 and its median SaCo to $-0.178$, whereas motion degraded the estimates far more without such a drop. Conclusions. Skin coverage and SaCo carry information complementary to the performance measures rather than a proxy for them: attributing to the skin does not guarantee an accurate estimate. What an attribution reveals about a condition is where the model looks rather than how faithfully its map is ordered.
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Artificial Intelligence · Cross-listed from cs.LG
Local Updates, Global Learning (LUGL): Playing Games with non-incremental Learners
David Milec, Spyridon Samothrakis, Michael Fairbank et al. (+1 more)
The dominance of Neural Networks (NNs) in RL is partially due to their incremental learning capability, which naturally suits the online, non-stationary nature of self-play training. However, gradient-boosted trees like LightGBM are widely recognised as the state of the art for tabular data in supervised learning, often outperforming NNs in accuracy and efficiency. Game states are inherently tabular—discrete actions, categorical card identities, structured board positions—which makes them an ideal candidate for tree-based methods. We introduce LUGL (Local Updates, Global Learning), a framework that decouples data collection from model fitting, enabling non-incremental learners such as GBTs to operate in RL settings where they would otherwise fail due to distributional shift. LUGL alternates between a local updates phase, where the
agent plays self-play games and accumulates tabular updates (Q-values, V-values, policies, or regret values) in a finite table, and a global learning phase, where the table is used to train a function approximator that generalises to unseen states before the table is reset. We test our approach in four standard perfect-information games (Tic-tac-toe, Connect-4, Othello, and Hex) and five imperfect-information games (Kuhn's poker, Leduc Hold'em, Liar's Dice, Goofspiel, and Flop5 Hold'em), and show that our results are competitive with or superior to DQN and DeepCFR. Our experiments demonstrate that the community's strong bias towards NNs in game-playing may be unwarranted, since LightGBM-based
agents achieve competitive or superior performance across all tested
benchmarks.
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All authors David Milec, Spyridon Samothrakis, Michael Fairbank, Dennis J.N.J. Soemers
Artificial Intelligence · Cross-listed from cs.CL
Enhancing Financial Question Answering: A Novel Benchmark Dataset of Banks' financial statements
Arianna Miola, Bruno Spaccavento, Lorenzo Silotto et al. (+2 more)
The comparative analysis of banks' financial statements poses significant challenges for automated question answering systems due to their complexity, substantial length, technical
language, and inhomogeneity of both textual and numerical content across different jurisdictions and institutions. We introduce FinRAG-QA, a novel
benchmark dataset for financial question answering, which comprises 999 practitioner-curated questions on 10 standardised indicators, grounded in 209 annual and Pillar 3 reports from 24 major European and U.S. banks spanning 2019-2023. Unlike prior financial QA
benchmarks, which centre on U.S. filings and single-institution analysis, FinRAG-QA targets cross-institutional
retrieval over documents averaging 198k words, longer than any existing financial QA resource. On this
benchmark we evaluate a multi-stage RAG pipeline and isolate the contribution of each component. Contextual chunk enrichment combined with a
retrieval-optimised
embedding model raises NDCG@10 from 0.322 to 0.710; conditional on the ground truth being retrieved, a
reasoning-optimised generator raises answer accuracy from 44.6% to 79.0% (+34.4 percentage points), at roughly 20x the generation latency. We further show that cross-encoder reranking degrades
retrieval when the first-stage ranking is already strong, and that a single top-ranked chunk outperforms larger contexts at generation time. Experiments were run in late 2024-early 2025 with the models available at that time.
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All authors Arianna Miola, Bruno Spaccavento, Lorenzo Silotto, Marco Bianchetti, Luca Cagliero
Artificial Intelligence
Analysis of Prompt Engineering for Drug Toxicity Prediction
Mia MacGregor, Aakash Welgamage Don and Mark Bartlett
Clinical trials in the UK can cost up to {\pounds}1.3 million, with approximately 90% drug failure rate. Toxicity is a major contributing factor in drug failure. Testing is time and cost intensive. In recent years, the use of artificial intelligence has been increasingly explored to aid in the prediction of drug toxicity, with extensive use of large
language models (LLMs). However, LLMs can show considerable variation when minor changes are made to
prompts, which raises concerns about their sensitivity to
prompt engineering.
Prompt engineering is used to optimise a
prompt given to an LLM to generate the desired output. This paper proposes a method to analyse
prompt engineering for drug toxicity prediction. The aim of the paper is to investigate the importance of
prompt phrasing for drug toxicity prediction. LLMs were prompted to identify chemical properties of significance when predicting drug toxicity.
Prompts were constructed to investigate; job role,
prompt structuring, and rule interpretation. LLMs were then used to generate
datasets, using the identified features from initial prompting, which were then passed to machine learning
algorithms. The experiments show that the natural variance which occurs in LLMs outweighs any fine-tuning of
prompts. There were, however, substantial improvements in model performance when using chemoinformatic code to extract features instead of using LLM-generated values. The proposed analysis methodology is applicable to a wide range of
prompt types across different areas of bioinformatics.
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Artificial Intelligence · Cross-listed from cs.CL
</think> Doesn't Stop Reasoning: Analysis of Spurious CoT Termination
Seunghee Koh, Sungjae Choi, Minchan Kwon et al. (+2 more)
Chain-of-thought (CoT)
reasoning improves large
reasoning models (LRMs) on complex tasks but often produces long, redundant traces. Recent training-free early-exit methods shorten these traces by choosing an intermediate point to stop
reasoning. We study one such strategy that injects an end-of-think
token (EoT, </think>) at this point to trigger the
reasoning-to-answering transition, and find that the injected EoT does not always induce a clean answering phase. Answering-phase generation can continue before the model regenerates another EoT, with the span preceding this regenerated EoT scaling with the
reasoning tokens saved by early exit and exhibiting continued
reasoning behavior. We call this spurious CoT termination, where
reasoning-like generation continues into the answering phase. We hypothesize that insufficient
attention to the injected EoT contributes to spurious CoT termination and probe this hypothesis with Exit-
token Attention Biasing (EAB). Across four LRMs, five
benchmarks, and two early-exit methods, increasing
attention to the injected EoT reduces spurious CoT termination and answering-phase length. These results reveal a limitation of controlling LRMs by externally matching their explicit think-block format. Inserting the EoT
token conforms to this format but does not by itself guarantee the intended
reasoning-to-answering transition. Our code is available at https://github.com/Seunghee-Koh/Spurious-CoT-Termination.
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All authors Seunghee Koh, Sungjae Choi, Minchan Kwon, Sunghyun Baek, Junmo Kim
Artificial Intelligence · Cross-listed from cs.CV
EraseSAE: Surgical Concept Erasure in Text-to-Video Diffusion Models via Sparse Autoencoders
Xinghao Wang, Dong Li, Wei Yu et al. (+5 more)
Recent advances in text-to-
video (T2V)
diffusion models have demonstrated remarkable generative capabilities, yet their reliance on loosely curated training data raises pressing safety and copyright concerns. Concept erasure offers a principled remedy by removing unwanted semantics from pretrained models while preserving remaining concepts. However, existing approaches typically operate at a coarse granularity misaligned with the fine-grained, distributed nature of concept representations, leading to incomplete removal or degraded generation quality. We argue that
surgical erasure fundamentally requires intervention at the level of monosemantic features, where each unit encodes a single interpretable concept. To this end, we propose EraseSAE, a novel framework that leverages sparse autoencoders to achieve
surgical concept erasure in DiT-based T2V
diffusion models via a principled decompose-attribute-erase pipeline. We first introduce the Partitioned Convolutional Sparse Autoencoder, which decomposes dense spatiotemporal activations into disentangled, interpretable sparse features while preserving spatiotemporal coherence. A contrastive attribution mechanism then contrasts activations from paired
prompts to isolate concept-specific feature kernels. At inference, timestep-resolved spatiotemporal masks derived from the identified kernels confine erasure to regions where the target concept is active, leaving unrelated content intact. Extensive experiments across diverse
diffusion models and concept erasure tasks demonstrate that EraseSAE achieves precise and robust concept removal with minimal quality degradation, substantially outperforming state-of-the-art methods. The code is available at https://github.com/HiDream-ai/EraseSAE.
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All authors Xinghao Wang, Dong Li, Wei Yu, Yingwei Pan, Tao Gong, Qi Chu, Nenghai Yu, Ting Yao