Artificial Intelligence
Content-Based Smart E-Mail Dispatcher Using Large Language Models
K. Paramesha, K R Sriram, Sujan Shetty et al. (+2 more)
Email communication has become an integral part of personal and professional life, but handling its vast volume is still a significant issue for large organisations. Manual perusal of emails and forwarding their contents and attachments to intended recipients using other instant messaging platforms has proved to be error-prone and time-consuming leading to losses in terms of productivity and creating undue stress. The main objective of this paper is to explore an alternative mechanism that is to automate the task of dispatching emails based on their contents to the respective WhatsApp groups of students of various semesters of programs in an engineering college, facilitating a smooth flow of information from one end to another end in an organisation. The dispatcher system is built using
agents querying large
language models (LLMs) to enable it to analyze the contents of emails and route them to the relevant groups of students for their information and consumption. The system harnesses the capabilities of LLMs in analysing the textual contents for decision-making. With a well-structured
agent framework
prompt that includes email content as input with instructions and context, the system figures out the relevant groups to which the email message is dispatched, thus providing the required information on time. The proposed system does not rely on labelled
datasets and provides several benefits, including enhanced productivity and a reduction in the cognitive load associated with reading emails.
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All authors K. Paramesha, K R Sriram, Sujan Shetty, Shamanth Kishore, R.Tejaswini
Artificial Intelligence · Cross-listed from cs.LG
SharQ: Bridging Activation Sparsity and FP4 Quantization for LLM Inference
Haoqian Meng, Yilun Luo, Yafei Zhao et al. (+4 more)
Low-bit floating-point formats and semi-structured sparsity are increasingly supported by modern accelerators, yet combining them for LLM activation compression remains challenging: activations contain input-dependent outliers that dominate block scales in FP4 quantization, and directly applying N:M sparsity masks discards moderate values, coupling sparsification loss with quantization error. We introduce SharQ, a training-free inference method that bridges activation sparsity and FP4 quantization through an online sparse–dense decomposition. For each activation tensor, SharQ generates an input-adaptive N:M mask to extract an outlier-dominated sparse backbone, quantizes it to FP4, and defines a dense residual relative to the quantized sparse backbone rather than the unquantized sparse values. A sparse FP4 GEMM processes the backbone while a dense FP4 GEMM compensates for both mask-induced activation loss and sparse-path quantization error. The two paths share a single FP4 weight payload with path-specific scale views, and a fused preparation kernel absorbs mask generation, residual construction, and layer normalization into one operator. SharQ requires no calibration data, retraining, or model-specific tuning. Evaluated on Llama-3.1-8B, Qwen2.5-7B, Qwen3-30B-A3B, and Qwen3-VL-8B, SharQ recovers 43–63% of the NVFP4-to-FP16 accuracy gap across
language and
vision-
language tasks, and generalizes across NVFP4, HiF4, and MXFP4 formats. On an RTX 5090, SharQ delivers 2.2–2.4$\times$ latency reduction over FP16 and 1.2–1.4$\times$ throughput improvement over FP8 in
language model serving, and up to 1.58$\times$ speedup on Wan2.2-T2V-A14B
video generation when combined with SageAttention. Our code is available at https://github.com/actypedef/SharQ.
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All authors Haoqian Meng, Yilun Luo, Yafei Zhao, Wenyuan Liu, Huaqing Zheng, Xindian Ma, Peng Zhang
Artificial Intelligence
A Multi-Level Validation and Traceability Framework for AI-Generated Telescope Scheduling Decisions
Hengchu Xiao and Chuanjun Wang
With the gradual introduction of AI into
telescope scheduling, AI-based decision-making has shown advantages in handling complex multi-constraint problems. However, its outputs often suffer from inconsistent data references,
reasoning errors, and non-executable decisions, limiting applicability in high-reliability observational tasks. In this work, we propose a multi-level validation and traceable
reasoning framework that performs systematic reliability verification of AI-generated decisions prior to execution, and enables explicit representation of the
reasoning process to support traceable decision-making. The framework integrates data reference validation, logical consistency checks, and observational and instrumental constraint verification to filter and correct invalid decisions. It also introduces atomic
reasoning units and their dependency relationships, representing scheduling decisions as a sequence of interconnected
reasoning steps that support error localization and post hoc analysis. Experiments show that the framework improves executability and reliability of AI scheduling and reduces loss of transient opportunities. In particular, feedback correction and structured validation of
reasoning steps enhance the ability to repair and block erroneous decisions, especially in complex scenarios. Compared with pure AI methods, the framework-enhanced approach maintains flexibility while substantially improving reliability and executability. These results demonstrate a feasible and verifiable pathway for applying AI to high-reliability astronomical observation scheduling.
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Artificial Intelligence
EvoOptiGraph: Weakness-Driven Coevolution via Graph-Based Structural Generation for Optimization Modeling
Qingcan Kang, Mingyang Liu, Xiaojin Fu et al. (+3 more)
Automating optimization modeling from natural
language with large
language models (LLMs) faces two key challenges. First, training corpora lack structural diversity. Second, data generation pipelines remain static and decoupled from model learning. To address these challenges, we propose EvoOptiGraph, a novel framework where data and model co-evolve, driven by model weaknesses. EvoOptiGraph represents each mixed-integer linear program (MILP) as an attributed bipartite graph and applies validity-preserving evolutionary operators to generate structurally diverse instances. The evolved graphs are converted into solver code and natural
language via deterministic compilation and verified back-translation. Training proceeds in two stages: supervised fine-tuning (SFT) on an initial
dataset, followed by reinforcement learning with verifiable rewards (RLVR), where graph-derived weakness signals guide the generation of new instances targeting the model's failures. This forms a closed loop that continuously updates the training distribution. Empirical results on six public
datasets show that EvoOptiGraph significantly outperforms larger generalist models, agentic methods, and specialized baselines in accuracy, executability, and generalization. These results demonstrate that targeted data-model coevolution is an effective strategy for improving LLMs on optimization modeling tasks.
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All authors Qingcan Kang, Mingyang Liu, Xiaojin Fu, Shixiong Kai, Tao Zhong, Mingxuan Yuan
Artificial Intelligence · Cross-listed from cs.RO
IDEA: Insensitive to Dynamics Mismatch via Effect Alignment for Sim-to-Real Transfer in Multi-Agent Control
Chenlong Liu, Zhuohui Zhang, Xinyan Chen et al. (+3 more)
Complex multi-
agent control tasks remain challenging for traditional rule-based and model-based approaches, motivating the adoption of learning-based methods. However, learning-based methods often struggle with sim-to-real transfer because they rely on accurate dynamics modeling or system identification and learn policies in low-level control spaces that are highly sensitive to dynamics mismatch, making them costly and fragile in complex environments. To address this issue, we propose a sim-to-real method for multi-
agent control, which is insensitive to dynamics mismatch via effect
alignment. Our method combines random environmental structure with discrete semantic actions through closed-loop control, elevating policy learning to a semantic abstraction level. Additionally, we develop an action synchronization mechanism that mitigates inter-
agent action timing mismatches, thereby enhancing the temporal consistency of the system. Experiments on four multi-
agent navigation tasks demonstrate that our method substantially improves training efficiency over mainstream transfer methods and achieves higher success rates in real-world scenarios, thereby improving the robustness and deployment stability of multi-
agent systems under dynamics mismatch.
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All authors Chenlong Liu, Zhuohui Zhang, Xinyan Chen, Zhipeng Wang, Bin Cheng, Bin He
Artificial Intelligence · Cross-listed from eess.SP
Multi-Modal Environment-Aware Beam Management for Massive MIMO: A Geometry-Driven Virtual Base Station Framework
Yijie Bian, Wei Guo, Jie Yang et al. (+4 more)
High-frequency massive multiple-input multiple-output (
MIMO) systems promise ultra-high data rates. However, efficient beam management remains challenging due to the prohibitive beam training overhead and intricate coordination required in multi-user
MIMO (MU-
MIMO) scenarios. To address these bottlenecks, environment-aware communications have emerged as a promising paradigm, leveraging site-specific knowledge to circumvent exhaustive pilot-based beam training and streamline multi-user communications. In this paper, we propose an interpretable and geometry-driven framework that utilizes multi-modal environmental data, specifically regional 3D light detection and ranging (
LiDAR) point clouds and location information, to construct an offline virtual base station (VBS) database. By modeling dominant reflection paths via mirror
symmetry across building facades reconstructed from the point clouds, the VBS database provides a compact and sparse description of the wireless propagation environment. To bridge the semantic gap between
geometric information and wireless channels, we develop a coarse channel reconstruction mechanism that estimates channel parameters directly from VBS-derived
geometric relationships. Based on the resulting coarse beamspace representation, we design a VBS-assisted orthogonal-pilot (VOP)-based partial beam training scheme to refine the coarse estimates with minimal online training overhead. Finally, to tackle the combinatorial beam selection problem and manage inter-user interference, we propose a hierarchical deep reinforcement learning framework, namely a dual-
agent dueling double deep Q-network, for coordinated beam selection (DD3QN-CBS). Simulation results demonstrate consistent gains in both beam training efficiency and beam selection performance over heuristic and learning-based baselines.
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All authors Yijie Bian, Wei Guo, Jie Yang, Shenghui Song, Jun Zhang, Shi Jin, Khaled B. Letaief
Artificial Intelligence · Cross-listed from q-bio.GN
scBench-Long: Verifiable Benchmarking of Long-Horizon Single-Cell Biology
Ian Diks, Zhen Yang, Arjun Banerjee et al. (+2 more)
Single-
cell studies require analysts to convert raw measurements into specific biological claims through multi-step workflows and integration of metadata, assay context, and auxiliary evidence. Existing AI-biology
benchmarks largely measure broad knowledge, executable workflows, or local analysis steps. We introduce scBench-Long, a
benchmark for long-horizon single-
cell biology in which
agents must recover scientific conclusions from raw or near-raw data without prescribed methods. The
benchmark contains 21 evaluations spanning melanoma CD8 T-
cell reactivity, CD8 RNA+ATAC regulatory inference, human–monkey chimera development, KRAS-driven lung tumor aging, and lethal COVID-19 lung pathology. Tasks cover paired scRNA/TCR sequencing, RNA and chromatin profiling, cross-species transcriptomics, combinatorial scRNA-seq, single-
nucleus RNA-seq, immune repertoires, ortholog maps, ligand–receptor resources, and validation evidence. Candidate claims are reproduced, reviewed, and converted into controlled answer vocabularies with deterministic grading and trajectory rubrics. Across 1,068 completed trajectories, the strongest model–harness pair passes 16/63 runs (25.4%). scBench-Long evaluates whether
agents can move beyond local analysis steps and make complex scientific claims that are supported by single-
cell data.
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All authors Ian Diks, Zhen Yang, Arjun Banerjee, Tim Proctor, Kenny Workman
Artificial Intelligence
Explainable Ensemble-Based Machine Learning Models for Detecting the Presence of Cirrhosis in Hepatitis C Patients
Abrar Alotaibi, Lujain Alnajrani, Nawal Alsheikh et al. (+5 more)
Hepatitis C is a liver infection caused by a virus, which results in mild to severe inflammation of the liver. Over many years, hepatitis C gradually damages the liver, often leading to permanent scarring, known as cirrhosis. Patients sometimes have moderate or no symptoms of liver illness for decades before developing cirrhosis. Cirrhosis typically worsens to the point of liver failure. Patients with cirrhosis may also experience
brain and nerve system damage, as well as gastrointestinal hemorrhage. Treatment for cirrhosis focuses on preventing further progression of the disease. Detecting cirrhosis earlier is therefore crucial for avoiding complications. Machine learning (ML) has been shown to be effective at providing precise and accurate information for use in diagnosing several diseases. Despite this, no studies have so far used ML to detect cirrhosis in patients with hepatitis C. This study obtained a
dataset consisting of 28 attributes of 2038 Egyptian patients from the ML Repository of the University of California at Irvine. Four ML
algorithms were trained on the
dataset to diagnose cirrhosis in hepatitis C patients: a Random Forest, a Gradient Boosting Machine, an Extreme Gradient Boosting, and an Extra Trees model. The Extra Trees model outperformed the other models achieving an accuracy of 96.92%, a recall of 94.00%, a precision of 99.81%, and an area under the receiver operating characteristic curve of 96% using only 16 of the 28 features.
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All authors Abrar Alotaibi, Lujain Alnajrani, Nawal Alsheikh, Alhatoon Alanazy, Salam Alshammasi, Meshael Almusairii, Shoog Alrassan, Aisha Alansari
Artificial Intelligence · Cross-listed from cs.CV
SpaceRipple: Lightweight Semantic Delivery for Mission-Oriented LEO Earth Observation Satellite Networks
Ziyi Yang, Hao Yuan, Yunxiang Yi et al. (+2 more)
Earth observation satellite networks generate massive volumes of high-resolution imagery, whereas inter-satellite and downlink resources remain limited. In many time-sensitive missions, ground users require mission-relevant semantic information rather than a full raw-image downlink. This paper proposes SpaceRipple, a lightweight framework for mission-oriented semantic delivery and on-board processing in Earth observation satellite networks. A sensing satellite performs adaptive compression and metadata generation to reduce inter-satellite traffic, while an edge computing satellite restores the received representation and extracts task-relevant semantic information. Unlike fidelity-driven image transmission, SpaceRipple coordinates compression, forwarding, restoration, and semantic inference within a collaborative pipeline, enabling semantic-oriented delivery instead of pixel-level image delivery. A compression-aware MoE enhancement module is further introduced to improve robustness under degraded visual inputs. Experimental results show that SpaceRipple achieves favorable reconstruction quality, improved semantic detection performance, and substantial bandwidth savings, demonstrating its potential for efficient and reliable Earth observation under constrained satellite-network resources.
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All authors Ziyi Yang, Hao Yuan, Yunxiang Yi, Wenbo Wang, Xing Zhang
Artificial Intelligence · Cross-listed from cs.CV
Perception, Verdict, and Evolution: Hindsight-Driven Self-Refining Forensics Agent for AI-Generated Image Detection
Yangjun Wu, Keyu Yan, Yu Liu et al. (+5 more)
The rapid advancement of generative models presents a significant challenge to existing deepfake detection methods, particularly given the widespread dissemination of highly realistic AI-generated images. Although
Multimodal Large
Language Models (MLLMs) show strong potential for this task, existing approaches suffer from two key limitations: insufficient sensitivity to fine-grained forensic artifacts and reliance on static synthetic supervision from frontier models, leading to limited flexibility and high-cost. To address these issues, we propose ForeAgent, an agentic forensics framework for AI-generated image detection with iterative self-
evolution. First, ForeAgent adopts a Perception-Verdict architecture that aggregates multi-view cues spanning semantic, spatial, and frequency-domain features, and leverages an MLLM as a verdict module to fuse these signals for a logical-grounded verdict. Second, to enable continual self-improvement, we introduce a Hindsight-Driven Self-Refining strategy following a Sampling-Reflection-
Evolution paradigm. The
agent performs inference rollouts on training instances. Guided by ground-truth labels as hindsight, it reflects on failure cases and low-quality
reasoning trajectories to regenerate higher-quality
reasoning traces. These synthesized samples are then strictly filtered through a dual-expert quality gating module. ForeAgent continuously evolves via fine-tuning on self-curated high-quality samples. Extensive experiments demonstrate that ForeAgent achieves state-of-the-art performance on the Chameleon
benchmark, reaching 82.18% accuracy (+16.41% over AIDE), and achieves 93.3% mean accuracy on AIGCDetect-
Benchmark across 16 generators. In addition, external evaluation shows that ForeAgent produces more consistent and causally grounded
reasoning compared to GPT-5 and GPT-5-mini.
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All authors Yangjun Wu, Keyu Yan, Yu Liu, Jingren Zhou, Fei Huang, Rong Zhang, Zhou Zhao, Fei Wu
Artificial Intelligence
PMDformer: Patch-Mean Decoupling Information Transformer for Long-term Forecasting
Ao Hu, Liangjian Wen, Jiang Duan et al. (+7 more)
Long-term time series
forecasting (LTSF) plays a crucial role in fields such as energy management, finance, and traffic prediction.
Transformer-based models have adopted patch-based strategies to capture long-range dependencies, but accurately modeling shape similarities across patches and variables remains challenging due to scale differences. To address this, we introduce patch-mean decoupling (PMD), which separates the trend and residual shape information by subtracting the mean of each patch, preserving the original structure and ensuring that the
attention mechanism captures true shape similarities. Futhermore, to more effectively model long-range dependencies and capture cross-variable relationships, we propose Trend Restoration
Attention (TRA) and Proximal Variable
Attention (PVA). The former module reintegrates the decoupled trend from PMD while calculating
attention output. And the latter focuses cross-variable
attention on the most relevant, recent time segments to avoid overfitting on outdated correlations. Combining these components, we propose PMDformer, a model designed to effectively capture shape similarity in long-term
forecasting scenarios. Extensive experiments indicate that PMDformer outperforms existing state-of-the-art methods in stability and accuracy across multiple LTSF
benchmarks. The code is available at https://github.com/aohu1105/PMDformer.
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All authors Ao Hu, Liangjian Wen, Jiang Duan, Yong Dai, He Yan, Dongkai Wang, Jun Wang, Yukun Zhang, Ruoxi Jiang, Zenglin Xu
Artificial Intelligence · Cross-listed from cs.LG
CascadeFormer: Depth-Tapered Transformers Motivated by Gradient Fan-in Asymmetry
Huzama Ahmad, Cao Viet Hai Nam and Se-Young Yun
Deep
Transformers are composed of uniformly stacked residual blocks, yet their deepest layers often add little value. We present two efficiency methods that exploit this asymmetry. CascadeFormer tapers width with depth to match the uneven information flow across layers, achieving comparable perplexity to a uniform baseline at the same training budget while reducing latency by 8.6% and increasing throughput by 9.4%. CascadeFlow
Pruning removes layers using accumulated training gradients, with no post hoc analysis. It outperforms standard heuristics on perplexity and rank-stability and stays competitive on downstream accuracy. To motivate these methods, we propose Gradient Fan-in Asymmetry (GFA) as a structural account of why deeper layers contribute less. In Pre-LayerNorm residual stacks, the gradient at a layer is the sum of an identity path and all downstream functional paths, producing a gradient fan-in that decays linearly with depth (and quadratically under deep supervision), yielding richer gradients for early layers and sparser ones for later layers. We provide correlational and interventional evidence for GFA on models trained from scratch up to 1.2B parameters. Across
Transformers and ResNets, accumulated training gradients follow the theoretical fan-in and are associated with post hoc layer importance. Two interventions point to structure rather than magnitude as the bottleneck: equalizing per-layer gradient norms does not restore late-layer value, while increasing downstream path counts via parameter-shared repetition restores and elevates it. Whether gradient magnitude proxies fan-in beyond high-rank regimes, and how these dynamics behave at the 100B+ scale, remain open questions.
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Artificial Intelligence · Cross-listed from cs.CV
From Hallucination to Grounding: Diagnosing Visual Spatial Intelligence via CRISP
Zhixing Li and Yinan Yu
Current VLM evaluations often conflate
language priors with genuine spatial
reasoning. To address this, we introduce CRISP, a novel structural-diagnostic evaluation paradigm that assesses visual spatial intelligence through consistency, the
alignment between implicit perception and explicit
reasoning. Unlike traditional black-box QA, CRISP utilizes metric 3D Scene Graphs and an oracle intervention protocol to decouple latent
reasoning capabilities from perceptual bottlenecks. This granular diagnosis uncovers a systematic perception-
reasoning disconnect. Crucially, we reveal that while proprietary models possess robust latent
reasoning engines, they suffer from inaccurate metric estimation and a critical failure to leverage their implicit structural representations. Conversely, open-source models remain fundamentally bottlenecked by their lack of multi-hop compositional
reasoning. By shifting the focus from merely “guessing correctly” via
language priors to genuinely “perceiving, verifying, and
reasoning,” CRISP offers a rigorous roadmap for
multimodal alignment beyond end-to-end post-training. The code and
dataset are available at https://github.com/iiyamayuki/CRISP-Bench.
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Artificial Intelligence · Cross-listed from cs.SD
VoiceTTA: Enhancing Zero-Shot Text-to-Speech via Reinforcement Learning-Based Test-Time Adaptation
Tianxin Xie, Chenxing Li, Dong Yu et al. (+1 more)
Recently, zero-shot text-to-
speech (TTS) has enabled high-fidelity and expressive
speech synthesis, but it often fails to imitate unseen speaking styles from uncommon scenarios (e.g., crosstalk, dialects). Moreover, fine-tuning pretrained models requires large, high-quality
datasets, limiting rapid personalization. We propose VoiceTTA, a reinforcement learning-based test-time adaptation (TTA) method that improves voice imitation of pretrained zero-shot TTS models. VoiceTTA introduces two style rewards based on coefficient-of-variation differences of F0 and energy, combined with speaker similarity and intelligibility (WER from a pretrained Whisper model), and optimizes learnable prefixes via group relative preference optimization (GRPO) in a flow matching-based model at inference time. Extensive experiments demonstrate substantial improvements on uncommon
speech prompts, outperforming state-of-the-art baselines. Audio samples are available at https://voicetta.pages.dev/
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All authors Tianxin Xie, Chenxing Li, Dong Yu, Li Liu
Artificial Intelligence · Cross-listed from cs.CL
DiARC: Distinguishing Positive and Negative Samples Helps Improving ARC-like Reasoning Ability of Large Language Models
Yuxuan Yang, Feiyang Li and Yile Wang
The Abstraction and
Reasoning Corpus (ARC) contains tasks that require summarizing patterns from limited grid samples and predicting output grids. Recently, many large
language model based approaches have attempted to transform it into a text-based
reasoning task. However, methods based on open-source models have generally yielded unsatisfactory results, while those relying on closed-source models are too costly. Current efforts mainly focus on data augmentation, constructing ARC-like data for more comprehensive supervised fine-tuning. In this work, we argue that solving ARC-like problems requires not only positive sample supervision but also the ability to improve model
reasoning by distinguishing negative samples. To this end, we draw on the idea of preference
alignment and propose DiARC, a method that constructs preference pairs to enable the model to distinguish between them. Specifically, we propose three ways to construct negative samples, including output-level visual transformations, DSL-level rule inversion, and task-specific rule editing. The resulting negative samples provide informative near-miss alternatives while keeping the observed demonstrations unchanged. Experimental results across multiple ARC-like
benchmarks show that DiARC consistently improves performance over baseline models. The code is released at https://github.com/szu-tera/DiARC.
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Artificial Intelligence · Cross-listed from cs.CL
The inattentional gap in task conditioned AI models that omit otherwise reportable safety critical signals
Kwan Soo Shin
AI in radiology and other safety-critical workflows is evaluated on the hazards it is told to find, yet harm arises disproportionately from hazards no one specified. We show that conditioning a
language or
vision model on a narrow task suppresses its reporting of co-present, safety-critical signals it can otherwise report, a behavioral analogue of human inattentional blindness. Across radiology text scenarios and thoracic-image
vision tasks, ordinary focused instructions suppressed reporting by up to 0.92; the gap ranged from minimal to complete across seven models, did not vary monotonically with scale, and persisted in a
reasoning model, while one flagship model showed a robust safety-reporting override. We term this dissociation the Inattentional Gap: a system can score near-perfectly on specified hazards while omitting co-present safety-critical hazards. In a 24-scenario probe, an independent open-ended critic restored every omitted finding. We propose reporting-complete evaluation as an admission criterion for safety-critical deployment.
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Artificial Intelligence
Radical AI Interpretability
Daniel A. Herrmann and Benjamin A. Levinstein
We develop a framework for interpreting AI systems as
agents, drawing on the philosophical tradition of radical interpretation and the tools of mechanistic interpretability. The core question is: given the computational facts about a system, how do we solve for its beliefs, desires, and meanings? This matters increasingly for safety. We want to be able to trust the systems we deploy, whether by understanding their goals or, more modestly, by reliably detecting deception. Interpretability researchers are building tools to read beliefs and desires off a model's internals, but there is no settled account of when such a tool has succeeded. This book supplies one. We propose criteria on both representationalist and interpretationist approaches, and tie each to tests current interpretability methods can carry out. A central lesson is that these attributions cannot be made piecemeal. Beliefs, desires, and the propositional structure they presuppose are jointly constrained, and a method that fixes one while measuring the others inherits whatever distortions that introduces. This holism becomes pressing for AI systems, which may not share the interpreter's concepts. However, it also provides leverage: a system's attitudes constrain its propositional structure, that structure constrains which attitudes can be attributed, and mechanistic interpretability can help us measure both.
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Artificial Intelligence · Cross-listed from cs.LG
Multipath Adaptive Gated Bottleneck Latent ODE with Raman Data Fusion for Cell Culture Process Forecasting
Johnny Peng, Thanh Tung Khuat, Ellen Otte et al. (+2 more)
Mammalian
cell-culture processes underpin the manufacture of many biopharmaceuticals, yet keeping a run on track is hard: critical process parameters drift over days, and an off-specification trend is often confirmed too late to intervene. Early-stage, multi-day forecasts could enable timely adjustment of feeding, sampling, and control, but bioprocess
forecasting is challenging because measurements are sparse and irregularly sampled, operating conditions are heterogeneous across
cell lines and media, and runs with near-identical early behaviour can diverge into different futures. We propose an adaptive framework combining a Gated Bottleneck Latent Ordinary Differential Equation (GB-Latent ODE) with Multi-Path Just-In-Time Fine Tuning (MP-JIT-FT). The GB-Latent ODE augments the stan dard Latent ODE with learnable variable-wise gating and a mask-aware bottleneck that compress high-dimensional sparse inputs, improving learning under limited data. Given a partially observed run, MP-JIT-FT retrieves similar historical trajectories, clusters the local neighbourhood into candidate regimes, and fine-tunes a separate model per regime to produce multiple plausible paths, each with a reconstruction-based confidence score, not a single averaged forecast. We further fuse Raman spectroscopy data: a machine-learning soft sensor turns dense Raman spectra into pseudo-observations that enrich the sparse offline measurements for more robust training. On 38 fed-batch 5L bioreactor runs spanning 14 conditions, MP-JIT-FT with Raman fusion achieves the best average rank and outperforms a global Latent ODE baseline on 8 of 9 target variables. Using local-divergence metrics, we show the multi-path gains are largest when locally similar prefixes diverge, whereas Raman fusion helps most when early dynamics are representative of later behaviour.
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All authors Johnny Peng, Thanh Tung Khuat, Ellen Otte, Katarzyna Musial, Bogdan Gabrys
Artificial Intelligence
What the LLM Should Not Say: Boundary-Aware Context Grounding for A Seven-Channel EEG Agent
Zhiyuan Xu, Yueqing Dai, Junling Li et al. (+1 more)
Large
language models (LLMs) can make scientific software easier to use. However, a general model does not automatically know which measurements a particular sensor can support, which
algorithms are implemented in the current software, or which conclusions are justified by a computed result. These distinctions are especially important for low-channel electroencephalography (
EEG), where sparse spatial coverage and variable signal quality make plausible but unsupported interpretations easy to produce. We present NeuraDock
Agent, an open-source architecture that separates a deterministic local
EEG engine from a hardware-aware
language layer. The numerical engine parses recordings, performs quality control, executes reviewed spectral workflows, and writes machine-readable artifacts. The LLM receives only a compact, allowlisted summary and a versioned context pack. The context describes the seven-channel hardware, reviewed workflows, result fields, implementation boundaries, scientific limits, and reference cases. Raw
EEG and dense per-sample arrays remain local We evaluate the system at three levels. First, 12 recordings produced identical structured results over ten numerical repetitions, and a complete Rest/Task run produced identical result, report, and figure hashes over three repetitions. Second, request-capture and failure-injection experiments confirmed the tested data boundary and preservation of local artifacts under HTTP, malformed-output, and connection failures. Third, a boundary-awareness
benchmark tested 36 ordinary and
adversarial questions under four context ablations and two LLMs, yielding 288 outputs.These results support hardware- and implementation-aware grounding as a practical mechanism for calibrating what an
EEG agent accepts, qualifies, or refuses; they do not establish clinical validity or a validated absolute cognitive-load index.
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All authors Zhiyuan Xu, Yueqing Dai, Junling Li, Junwen Luo
Artificial Intelligence
NeuraDock Visual Cognitive Load Agent Tutorial: A Quality-Gated Open-Source EEG Workflow for Alpha Dynamics and Real-Time Applications
Zhiyuan Xu, Yueqing Dai, Junling Li et al. (+1 more)
This tutorial paper provides a step-by-step, reproducible walkthrough of NeuraDock
Agent, an open-source
EEG agent focused on Alpha dynamics and visual cognitive-load analysis. The goal is practical: a reader should be able to install the
agent, run
EEG preprocessing and quality control, generate Alpha dynamics figures, perform within-subject Rest/Task visual cognitive-load comparison, run the public mini-
dataset analyses and compare them with the reference validation summary, start an online dashboard, call the real-time API from an external application, and use the LLM interpretation layer to explain quality risks. Existing
EEG toolkits provide excellent offline analysis, but assembling a real-time, quality-gated cognitive-load pipeline often requires manually bridging acquisition, custom QC, Alpha feature extraction, and a web API; this tutorial closes that offline-to-online gap. The tutorial uses a quality-gated workflow: downstream Alpha and workload metrics are computed only after preprocessing and QC gating rather than directly from raw
EEG. In the included mini-
dataset validation, the
agent processed 18 recordings, generated 10 within-subject comparisons, observed task-related posterior Alpha suppression in 7 of 10 contrasts, estimated initial evidence of within-subject repeatability, and benchmarked local online API latency. The tutorial is intended for researchers, developers, and applied teams who want a transparent path from
EEG files to real-time visual cognitive-load prototypes.
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All authors Zhiyuan Xu, Yueqing Dai, Junling Li, Junwen Luo
Artificial Intelligence · Cross-listed from cs.CL
Temporal Validity in Retrieval Memory: Eliminating Stale-Fact Errors for AI Agents over Evolving Knowledge
Neeraj Yadav
Retrieval-augmented generation (RAG) gives
agents access to accumulated knowledge, but has no model of time. When a fact changes (e.g., a function is renamed or API restructured), RAG retrieves both the stale and current value with near-identical
embedding similarity. The
agent then either abstains or serves the superseded fact. We show this is a structural problem: on a calibrated
dataset, cosine similarity distinguishes a contradicted fact from a duplicated one with AUROC 0.59 (near chance), as contradictions are often more
embedding-similar to the original than rephrased duplicates. We present MemStrata, a
retrieval memory maintaining temporal validity. It stores facts like RAG, preserving static recall, but when a fact's value is contradicted, a deterministic (subject, relation, object) supersession rule retires the stale value in a bi-temporal ledger - with no similarity threshold and no LLM call. Across six
benchmarks run locally with a 7B model, MemStrata ties RAG on static knowledge and reaches 0.95-1.00 accuracy on evolving knowledge (where RAG reaches 0.20-0.47). The central result is the stale-fact-error rate: when required to answer, RAG serves superseded values 15-40% of the time; MemStrata drives this to ~0%, a failure class RAG cannot avoid. MemStrata achieves this at
retrieval latency (~2.1s) versus ~16-18s for LLM-reranking baselines. We release the harness,
datasets, and a marker-free evaluation protocol for
memory under knowledge
evolution.
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Artificial Intelligence
Humans Disengage, Reasoning Models Persist: Separating Difficulty Registration from Deliberation Allocation
Han-yu Wang
Large
reasoning models (LRMs) tend to produce longer
reasoning traces on problems that also take humans longer. This correspondence leaves open how the systems distribute further work on those problems. We distinguish *difficulty registration*, sensitivity to differences in problem difficulty, from *deliberation allocation*, the distribution of further work once difficulty is encountered. We examine both in item-matched data from three
reasoning tasks. In visual abstraction (H-ARC), model trace length follows the human ordering of problems by duration. After item identity is controlled, successful human attempts last longer than failed attempts, while failed LRM attempts have longer traces than successful ones in the pooled model analysis. The estimated slopes follow the same pattern in intuitive
reasoning (INTUIT). In relational
reasoning (Cortes), successful attempts are longer in separate human and model analyses, while a joint fit on shared items finds a human-LRM difference. Longer human attempts include more grid actions. At comparable lengths, failed LRM traces contain more hedging on H-ARC and more repetition on Cortes. A resource-rational account relates these patterns to what further work is expected to achieve and how that progress is valued. The results identify a difference in the allocation of continued work that cross-problem duration
alignment alone leaves undetected.
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Artificial Intelligence
Clinical Harness for Governable Medical AI Skill Ecosystems
Tianhan Xu, Lei Bao, Zhe Hu et al. (+2 more)
Medical AI remains organized around isolated models, whereas care requires accountable capabilities that persist across time. We define clinical AI skills and propose the Clinical Harness, a runtime governance architecture that registers, orchestrates, constrains and monitors them. Using osteoporosis as an exemplar, we show how knowledge-driven, data-driven and physics-enhanced skills can support lifecycle care and provide a governed substrate for future medical
agents.
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All authors Tianhan Xu, Lei Bao, Zhe Hu, Tian Shen, Yongxiang Wang
Artificial Intelligence · Cross-listed from cs.SE
Evaluation-Strategy Gap in Fault Diagnosis of Deep Learning Programs
Sigma Jahan
Deep Learning (DL) programs can fail during training for many reasons, and diagnosing the cause is a costly and time-consuming maintenance task. Techniques for diagnosing such failures are commonly assessed using within-program cross-validation, which may be inadequate for deployment settings involving previously unseen programs. It is therefore necessary to assess how performance differs across these settings and to identify the causes of any performance gap in established fault diagnosis techniques for DL. We investigate this gap using DynFault, a corpus of 5,542 fault-injected training traces from 38 real-world DL programs. We found a gap of 0.190 in balanced accuracy for existing fault diagnosis techniques between within-program evaluation and holding out whole programs. We also found the gap comes from program-level structure in the features, which led us to examine two runtime feature sets,
curvature features and optimizer features, and their behavior on unseen programs. We found that
curvature features are useful for instability detection on unseen programs, while optimizer and activation features help only on programs seen during training.
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Artificial Intelligence · Cross-listed from cs.SE
An Empirical Study of LLM-Generated Specifications for VeriFast
Wen Fan, Minh Tran, Sanya Dod et al. (+5 more)
Static verification tools can assure industrial scale software, but require significant human labor to write specifications. This is particularly true of static verifiers based on separation logic (SL verifiers), which excel at verifying heapmanipulating programs, but require many complex auxiliary specifications to reason about heap structure. Recent work applies large
language models (LLMs) to generate code, tests, and proofs, including specifications for verifiers, but mostly targeting non-SL verifiers. To address this gap, this paper thoroughly evaluates how well LLMs perform when prompted to generate specifications for verifying 303 C functions with the SL verifier VeriFast. We explored eight prompting approaches, ten LLMs, and three input types in two stages. Quantitative and qualitative analyses are used to assess the LLM-generated code and specifications for functional behavior, verifiability and errors. The results show that LLMs preserve functional behavior in source code and specifications (both over 91%), but achieve modest verification success (31.4%). Using Gemini 2.5 Pro and providing formal contracts lead to higher success rates in our setting. Moreover, most errors (94%) come from LLMs' mistakes in the domainspecific knowledge of SL verifiers such as VeriFast. These findings provide guidance for optimizing LLM-generated specifications for SL verifiers.
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All authors Wen Fan, Minh Tran, Sanya Dod, Xin Hu, Marilyn Rego, Danning Xie, Jenna DiVincenzo, Lin Tan
Artificial Intelligence · Cross-listed from cs.CL
Speaking Numbers to LLMs: Multi-Wavelet Number Embeddings for Time Series Forecasting
Defu Cao, Zijie Lei, Muyan Weng et al. (+2 more)
Large
language models (LLMs) are attractive for context-aware time series
forecasting because they can integrate heterogeneous textual signals, yet their discrete,
language-oriented tokenization and
embedding interfaces are misaligned with continuous numerical values, often harming numerical ordering and
forecasting reliability. We propose TempoWave, a plug-and-play temporal wavelet digit interface that maps each scalar observation into digit-wise
embeddings constructed from multi-wavelet, multi-scale coefficients. By directly overriding standard
token representations, TempoWave seamlessly exposes both fine-grained local fluctuations and macro global structures in a
transformer-compatible form, ensuring that precise numerical formatting, distinct digit identity, and robustness to common normalization operations are maintained throughout the LLM pipeline. Experiments across five context-enriched
forecasting benchmarks demonstrate that TempoWave consistently improves LLM-based forecasters over standard numeric tokenization and alternative
embedding interfaces, achieving a new state-of-the-art. These results highlight the numeric interface as a key bottleneck and suggest that principled multi-resolution
embeddings can better couple LLMs' contextual
reasoning with precise
forecasting. Our code is available at https://github.com/DC-research/TempoWAVE and our model can be accessed at https://huggingface.co/Melady/TempoWAVE.
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All authors Defu Cao, Zijie Lei, Muyan Weng, Jiao Sun, Yan Liu
Artificial Intelligence · Cross-listed from cs.CR
Adaptive Evaluation of Out-of-Band Defenses Against Prompt Injection in LLM Agents
Praneeth Narisetty, Shiva Nagendra Babu Kore, Uday Kumar Reddy Kattamanchi et al. (+1 more)
Recent work (2024 to 2026) has converged on a strategy for defending tool-using LLM
agents against indirect
prompt injection: rather than training the model to refuse malicious instructions, enforce security outside the model with a deterministic policy that mediates the
agent's actions. Systems such as CaMeL, FIDES, Progent, RTBAS, and FORGE realize this with capabilities, information-flow labels, and reference monitors, and several report near-elimination of attacks on the AgentDojo
benchmark. We make two contributions. First, we organize these out-of-band defenses as instances of classical integrity protection (Biba), reference monitoring, and least privilege, yielding a structured comparison of what they do and do not cover. Second, we warn that every one of them is validated only on static
benchmarks (a fixed set of injection attempts), the same methodology that made in-band defenses look strong until adaptive, defense-aware attacks broke twelve of them at over 90% success; we specify the threat model and protocol an adaptive evaluation requires. We then run that protocol as an independent reproduction and extension of Progent's own adaptive-attack analysis, on AgentDojo, with an open-weight
agent (Qwen2.5-7B) self-hosted on a single H200, a setting its authors did not test. Averaged over three runs, the defense held: Progent cut mean attack success roughly sixfold (25.8% to 4.2%), and a hand-crafted adaptive attack did not raise it (2.6%). This is one small-scale data point on a weak model with a single black-box attack template; a stronger optimized (white-box GCG) attack remains open. The result is consistent with, but does not establish, the hypothesis that deterministic out-of-band enforcement is a harder target for an adaptive attacker than in-band detection.
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All authors Praneeth Narisetty, Shiva Nagendra Babu Kore, Uday Kumar Reddy Kattamanchi, Jayaram Kumarapu
Artificial Intelligence · Cross-listed from cs.LG
Retrieval-Warmed Energy-Based Reasoning: A Five-Arm Ablation Methodology for Diffusion-as-Inference on Structured Reasoning Tasks
Libo Sun, Po-Wei Harn, Zewei Zhang et al. (+2 more)
Warm-started
diffusion samplers accelerate iterative inference, but it is rarely clear which part of the pipeline carries the gain. We study
retrieval-warmed energy-based
reasoning (RW-EBR) – an IRED energy-based
diffusion model \cite{du2024ired} augmented with a Modern Hopfield trajectory
memory – and contribute a five-arm ablation methodology (oracle, best-constant, per-query-random, shuffled, aligned) that separates three confounded effects: class-prior bias shift,
stochastic warm-starting, and graph-aligned value reuse. The diagnostic decomposition is adapted from LLM-RAG evaluation \cite{ru2024ragchecker}. On connectivity-2 (Erdős–Rényi all-pairs reachability), the aligned-vs-shuffled-oracle swing reaches \textbf{$+35$\,pp} balanced accuracy on a fixed 1{,}000-graph validation-set diagnostic, with value distribution and
retrieval mechanics fixed, only per-graph
alignment destroyed, while per-query random initialisation falls below cold – per-graph
alignment, not bias shift or stochasticity, dominates. Yet the deployable cold-prediction pipeline misses the acceptance gate at stored-value quality. The same diagnostic logic, stopped at the key-quality screen, applied to Sudoku with a task-specific key encoder produces a clean negative at a different component – key quality, under the current setup. The decomposition names the first blocking component on each task. The setting – graph reachability refined by an iterative
diffusion sampler, with explainability of failure modes as the lens – places the work within structured and spatio-temporal
reasoning.
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All authors Libo Sun, Po-Wei Harn, Zewei Zhang, Peixiong He, Xiao Qin
Artificial Intelligence · Cross-listed from cs.LG
Localizing RL-Induced Tool Use to a Single Crosscoder Feature
Andrii Shportko, Shubham Bhokare, Ahmed Zeyad A Alzahrani et al. (+3 more)
Fine-tuning through RL reshapes the internal representations of
language models to enable agentic behaviors such as tool use, yet the mechanistic basis of these changes remains poorly understood. While RL substantially improves structured tool-call generation, it is unclear which features emerge, which are preserved, and whether identified features can be leveraged for retraining-free behavioral control. In this work, we show that $\textit{Dedicated Feature Crosscoders (DFC)}$ isolate a compact set of RL-specific features that mediate tool-calling capability in $\texttt{Qwen2.5-3B}$. Across a $48$-crosscoder hyperparameter sweep, encode-decode reconstruction improves the RL model's tool correctness by $+31.1 \pm {9.7}$ pp and passively transfers tool-calling ability to the frozen base model by $+6.8 \pm 5.0$ pp which we call a $\textit{capability spillover}$. Our findings show that DFC partitioning concentrates RL-introduced capability into a minimal, steerable feature set that enables runtime behavioral control of agentic LLMs.
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All authors Andrii Shportko, Shubham Bhokare, Ahmed Zeyad A Alzahrani, Bowen Cheng, Gustavo Mercier, Jessica Hullman
Artificial Intelligence · Cross-listed from cs.DB
3D Spatial Pattern Matching
Nicole R. Schneider, Avik Das, Lukas Arzoumanidis et al. (+3 more)
Spatial pattern matching is the process of matching query entities and constraints with database entities and relations. It has many applications, including similar region search, housing
market search, landmark search, and road network matching. To our knowledge, all existing spatial pattern matching approaches frame the problem in a 2 dimensional space, where entities lie in a cartesian plane and relationships defined between them are contained in 2 dimensions. However, this problem framing has significant limitations when searching for real world entities that have height in addition to position. To address this limitation, we extend spatial pattern matching to 3 dimensions and provide a generalized definition of the problem. We describe a subgraph matching
algorithm capable of resolving 3D spatial patterns over distance relations and release two 3D spatial pattern matching
datasets, one synthetic and one containing real 3D building data from the city of Hamburg, Germany. We test our subgraph matching
algorithm on both
datasets and present results as a baseline for future methods to build upon.
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All authors Nicole R. Schneider, Avik Das, Lukas Arzoumanidis, Abhijeet Ghodgaonkar, Hanan Samet, Youness Dehbi