Artificial Intelligence · Cross-listed from cs.LG
How Query Visibility Changes KV-Cache Compression Rankings: A Matched-Budget Audit
Daming Luo, Christy Liang and Junyu Xuan
KV-cache compression methods are predominantly evaluated with the query appended to the context before compression – a query-aware protocol. Yet the economic case for a compressed KV cache is reuse: compress a document once, answer many future questions against it. In that deployment, compression must happen query-agnostic – before any question is seen. We present a matched-budget audit of six published compression methods against three trivial baselines on three open 7-9B models (144,300 paired evaluations on RULER-8192; 40,800 on LongBench; 50,000-resample paired bootstrap throughout). Everything is held fixed – model, compression ratio, instances, decoding – except the scoring rule. Three findings. (1) Query visibility changes the rankings: under the agnostic protocol, of the five audited methods that share a common
attention backend, only KeyDiff beats a best-of-3 trivial baseline consistently (31 of 36
cells), and the most widely deployed method, SnapKV, loses to "keep the start and the recent window" on average (-0.066). (2) The per-method drop between the two protocols is ordered consistently with how visible the question is to each method's scoring signal, legible in its source code: from Delta=+0.198 for SnapKV (the question sits inside its 64-
token observation window) down to Delta=+0.011 for KeyDiff (its score contains no query term at all).
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Artificial Intelligence · Cross-listed from cs.LG
CARE-LoRA: Compressed Activation REconstruction for Memory-Efficient LoRA
Gengyu Zhang, Haiyin Ran, Zhengbao He et al. (+4 more)
As the scale of large pre-trained models continues to grow, fine-tuning them under limited
memory budgets has become increasingly challenging. Low-Rank Adaptation (LoRA), currently one of the most widely adopted parameter-efficient fine-tuning (PEFT) methods, mitigates this challenge by optimizing only low-rank adaptation
matrices, thereby greatly reducing the number of trainable parameters. With the parameter overhead substantially reduced, the activations retained for backpropagation have emerged as the primary remaining
memory bottleneck during LoRA fine-tuning. To address this, we propose CARE-LoRA, a data-aware Compressed Activation REconstruction framework. By exploiting the inherent projection structure of LoRA, CARE-LoRA replaces the full input activation with the low-rank compressed activation naturally produced by the LoRA branch. It further computes a lightweight reconstruction
matrix during the forward pass with negligible additional computation cost, which is used during backpropagation to reconstruct the gradient signal, thereby keeping LoRA
matrices fully trainable. Extensive experiments across diverse models and downstream tasks demonstrate that, while substantially reducing the overall
memory footprint, CARE-LoRA achieves competitive or even superior performance compared with standard LoRA and representative LoRA variants. Our code is publicly available at https://github.com/fishandyu/CARE-LoRA .
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All authors Gengyu Zhang, Haiyin Ran, Zhengbao He, Yuhang Liu, Hanling Tian, Zhehao Huang, Xiaolin Huang
Artificial Intelligence · Cross-listed from cs.LG
Mathematics of Data Science
Afonso S. Bandeira, Amit Singer and Thomas Strohmer
This book is about the mathematical foundations of data science. 1. Introduction 2. Curses, Blessings, and Surprises in High Dimensions 3. Singular Value Decomposition and Principal Component Analysis 4. Linear Regression and Regularization 5. Graphs, Networks, and Clustering 6. Nonlinear Dimension Reduction and
Diffusion Maps 7. Linear Dimension Reduction via Random Projections 8. Optimization for Data Science 9. Classification 10. A Mathematical Introduction to Deep Learning 11. Large Sample Limit of Graph
Laplacians 12. Community 13. Concentration of Measure and Gaussian Analysis 14.
Matrix Concentration Inequalities 15. Compressive Sensing and Sparsity 16. Low-Rank
Matrix Recovery
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Artificial Intelligence · Cross-listed from cs.DB
Confining Nondeterminism: AI-Driven Research Systems as DBMSs for Reliable, Non-Wasteful, Transparent, and Collaborative Research [Vision]
Kyoungmin Kim and Anastasia Ailamaki
LLM
agents that conduct research (proposing ideas, writing and running code, analyzing results) can already carry a study from research question to figures, yet cannot be fully trusted. The same question asked twice in a row returns different answers; the
agent announces a number that no execution produced, and tool use does not prevent this, because nothing binds what the
agent reports to what its tools returned; a small upstream change leaves downstream results silently stale, with no way to list which ones; and the
agent re-runs preprocessing and rewrites code it has already produced. We argue these failures share one root: every step of today's
agent loop is a
stochastic LLM call whose internal state nobody, including the
agent, can check. Rather than trying to see inside the LLM, we take a lesson from databases, which earn trust without being watched, because deterministic operators over well-defined state make their guarantees hold by construction. We propose organizing a research project the same way. The project lives in a deterministic, versioned dataflow engine (in effect, a query plan over materialized views), and the LLM, together with the user, is a
stochastic compiler that may only edit that plan. The executor never calls the LLM; LLM output enters only as versioned code and data that the executor then runs, and any asserted result enters the record only with an execution behind it. Five design rules at this boundary turn familiar database machinery, from versioning and provenance to incremental maintenance and cost-based scheduling, into guarantees that make research reliable, non-wasteful, transparent, and collaborative. This report presents the diagnosis, the requirements, and the design; the guarantee walkthrough, a prototype, and the research agenda appear in the full version, in preparation. The LLM, we argue, should be the query compiler, never the executor.
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Artificial Intelligence · Cross-listed from cs.CR
Temporary Authority, Permanent Effects: Commit-Time Authorization for LLM Agents
Igor Santos-Grueiro
An LLM
agent often acts on an approval it observed earlier: it reads the approval, prepares the request, and commits later. If the approval was withdrawn, or covers a different effect, the service can apply the change and report success. Seventeen fixed advisories from three deployed
agent products document failures: an approval did not cover the executed effect, with no intervening change (12), or survived a change in the facts it depended on (5). We propose commit-time authorization (CTA), requiring at least one complete, current justification for the exact effect at commit: a live grant, current premises, ready dependencies, an exact-effect binding, and an eligible path. CommitGate selects one complete justification. The sink validates it or consumes an expiring, single-use derived grant atomically with the effect. In a five-model service study whose
prompt states the authorization rule, timely notice prevented nearly all unauthorized effects for GPT, Fable, and Sonnet, and fewer for Qwen and Gemma. Without a signal, all five mostly completed tasks with unauthorized effects, even when a read could reveal the change; Fable's counts use its provider's answered requests. Protected sinks enforced the declared policy in LangChain approval flows, OpenHands coding tasks, services under cloud contention, and a transaction-runtime integration. The services and runtime refused effects that lost authority after a positive check and preserved fresh alternatives. After refusal, GPT, Fable, and Sonnet on OpenHands mostly published to a separately authorized target. Machine-checked proofs show whole-candidate selection, trace safety for mediated atomic sinks, and exact single-use transfer. With honest issuers, unique candidate IDs, and a premise required by policy, every candidate CTA accepts in the model carries a live grant given at the current version; slot-by-slot checks do not ensure this.
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Artificial Intelligence · Cross-listed from cs.LG
ARMOR: Stabilizing On-Policy LLM RL with Off-Policy Anchor Samples
Kexin Huang, Junkang Wu, Jinda Lu et al. (+7 more)
Reinforcement learning (RL) has significantly enhanced the
reasoning capabilities of large
language models (LLMs), yet the training process remains notoriously fragile. In this work, we investigate a critical source of this instability: over-optimization, where models exploit training heuristics at the expense of generalizable
reasoning. While reverse KL regularization is the standard defense against such degradation, our analysis reveals that it is often insufficient in this regime, as it fails to ensure comprehensive coverage of the reference distribution. To address this, we propose ARMOR (Anchor Rollout and Mixed Optimization for RL), a framework that shifts the paradigm from passive penalty to active sample stabilization. ARMOR comprises two key components: (1) Anchor Rollout, which leverages off-policy data from the reference policy to preserve established solution patterns; and (2) Mixed Optimization, which reformulates the policy objective to enable controlled exploration without relying on auxiliary losses. Extensive experiments on
reasoning benchmarks validate that ARMOR effectively mitigates validation collapse, enabling sustained performance improvements over extended training horizons.
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All authors Kexin Huang, Junkang Wu, Jinda Lu, Shuo Yang, Chiyu Ma, Jiancan Wu, Xiang Wang, Xiangnan He, Guoyin Wang, Jingren Zhou
Artificial Intelligence · Cross-listed from cs.LG
Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards
Pengfei Cai, Utkarsh Utkarsh, Alan Edelman et al. (+2 more)
Partial differential equations (PDEs) are foundational to modeling in science and engineering, but constructing reliable numerical solvers remains labor-intensive, demanding expert knowledge of discretization schemes, stability conditions, and boundary treatments. Recent work has begun to frame PDE solving as a code-generation task for large
language models (LLMs), yet existing approaches operate primarily at inference time: relying on prompting, debugging, self-refinement, and test-time scaling rather than adapting the model itself. In parallel, reinforcement learning with verifiable rewards has emerged as a post-training paradigm for code and math
reasoning, but its verifiers are typically binary: a compiler runs, or a test passes. Such signals discard the graded structure of scientific correctness, where two solvers may both execute and yet differ in solution accuracy by orders of magnitude. In this work, we introduce RLVP: Reinforcement Learning with Verifiable Physics, an RL post-training framework for multi-PDE solver code generation. RLVP addresses this verifiability gap with a hybrid verifier: hard program-validity checks ensure executability, while continuous physics rewards score function-space accuracy and PDE-residual consistency. A single policy is post-trained across diverse PDE families spanning
hyperbolic,
parabolic,
elliptic, and incompressible-flow systems. RLVP improves over both pre-trained and supervised-only baselines on PDE
benchmarks, and shows zero-shot improvement transfer to held-out PDEs. We show that a smaller LLM post-trained with RLVP can outperform prompting a frontier model on in-distribution PDE solver generation. The trained policy shows evidence of compositionality in numerical motifs: it recombines stencils, time-stepping schemes, and boundary-handling primitives learned from the PDEs used in training into generated solvers for unseen PDE problems.
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All authors Pengfei Cai, Utkarsh Utkarsh, Alan Edelman, Christopher Vincent Rackauckas, Rafael Gomez-Bombarelli
Artificial Intelligence
GRASP: GRanularity-Aware Search Policy for Agentic RAG
Varun Gandhi, Jaewook Lee, Shantanu Todmal et al. (+4 more)
Agentic
retrieval-augmented generation (RAG) extends static RAG by allowing
language models to iteratively reason, generate search queries, retrieve evidence, and predict answers. However, it remains challenging for models to decide when to retrieve, whether to use lexical matching or semantic similarity, and how to control context granularity to prevent irrelevant
tokens from interfering with
agent reasoning. In this paper, we introduce GRASP, a reinforcement learning (RL) framework for training
agents to adaptively coordinate complementary
retrieval tools during multi-step
reasoning. GRASP provides the
agent with semantic search, keyword search, and paragraph-reading actions, enabling it to retrieve sentence-level evidence and expand further context only when needed. We train the policy with a reward that jointly accounts for answer accuracy, grounded reading, complementary search, and turn efficiency. Experiments on multi-hop
reasoning benchmarks show that GRASP improves both
retrieval recall and downstream question answering performance compared with single-step
retrieval, prompting-based agentic RAG, and RL-based
retrieval baselines. Qualitative and ablation analyses show that the learned policy develops interpretable skimming and scanning behavior: it uses semantic search for broad exploration, paragraph reading for local verification, and keyword search for entity-specific evidence. These results suggest that learning to coordinate
retrieval signals and context granularity is critical for
agent's correct
reasoning.
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All authors Varun Gandhi, Jaewook Lee, Shantanu Todmal, Franck Dernoncourt, Ryan Rossi, Zichao Wang, Andrew Lan
Artificial Intelligence · Cross-listed from cs.CV
Annotation-Free Furniture Codes: What They Encode, and How Far They Transfer
Benjamin Friedman
Layout-based 3D scene synthesizers place each object using two human-annotated channels: a categorical class label and a canonical-pose convention. We ask whether a single self-supervised
token derived from object geometry can replace both, and study such
tokens directly as a representation, decoupled from any synthesizer. A Finite Scalar Quantization (FSQ) point-cloud autoencoder is chamfer-trained on placed 3D-FUTURE furniture with no labels or pose annotations. Diagnostic probes recover fine-category (62.6 +/- 0.5%), super-category (85.6 +/- 1.3%), and yaw (52.7 +/- 0.5 deg) from the codes alone. Swapping the chamfer target from the rotated to the un-rotated point cloud collapses the yaw signal while raising class recovery, showing the codes' rotation content can be set by the training objective. Scaling across asset libraries needs codes that transfer; on an unseen
dataset (ShapeNet),
alignment is category-dependent: box-like furniture transfers, organically-shaped furniture does not, and a target-blind augmentation partly closes the gap.
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Artificial Intelligence
ANCHOR: Automated Alignment Auditing for CLI Agents on Real-World Harm
Kefan Song and Yanjun Qi
Autonomous CLI
agents can now execute hundreds of actions across multi-hour sessions: writing code, executing shell commands, browsing the web, and managing cloud infrastructure, all with minimal human oversight. Does greater autonomy invite greater risk? We introduce ANCHOR, an automated
auditing framework that stress-tests CLI
agents on illegal tasks grounded in public US court cases. ANCHOR deploys an auditor
agent fine-tuned on dark personality data using supervised and reinforcement fine tuning. This auditor roleplays persistent malicious users who decompose tasks, reframe requests upon refusal, and adapt strategies across multi-turn interactions. Evaluating frontier CLI
agents, we find that while they often refuse illegal tasks when prompted directly, compliance reaches 100% under persistent malicious interaction. When
agents comply, they frequently exceed user requests, autonomously building infrastructure for large-scale harm, including catastrophic risk scenarios such as large-scale financial fraud and bioweapon development. These findings demonstrate that current
alignment techniques are insufficient for autonomous
agents and underscore the need for safety evaluations against persistent, adaptive malicious users. We release ANCHOR at https://github.com/garified/anchor
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Artificial Intelligence · Cross-listed from cs.LG
Context by Distinct Information: An Auditable Dirichlet-Process Working Memory for Long, Redundant Context Streams
Siddharth Pal and Viktoria Rojkova
Context engineering decides what information a model carries forward, and current designs meter it in
tokens: compressing the past into a bounded recurrent state, keeping a key-value entry for every
token, or imposing a fixed budget through a window or eviction rule. All three make the
token the unit of
memory even when the stream is redundant and the task depends on the distinct information it carries. Building on a companion mechanism paper that opens a cache slot only when an incoming key is novel, so
memory scales with the number of distinct items rather than
tokens, we develop that allocate-on-novelty cache as a working-
memory component and organize context by how a task depends on the past: recall-carried information belongs in a content-addressed novelty cache, summary-carried information in a recurrent state, and locality-carried information in a recency window. The claim is empirical and bounded. On a matched character-level control, novelty-gated
attention reaches full-
attention performance while attending to about half the
tokens, and coupling the cache with a state-space summary matches full-
attention coupling at that reduced cost; the advantage grows as context lengthens, while a sliding window is preferable on short, locality-dominated spans. On next-code prediction over synthetic Medicare claims the coupled component leads full
attention and every fixed-budget eviction policy at a thousand-event horizon, whereas cost
forecasting over the same stream is summary-carried and the cache is neutral. The retained
memory is an inspectable table of templates, codes, drugs, or places rather than an opaque state. The experiments are small-scale and use only public data; they establish the primitive that context can scale with distinct information rather than
tokens, in a working
memory that is content-addressable and auditable.
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Artificial Intelligence · Cross-listed from q-bio.NC
A Physics-Inspired Classical Digital Twin of Cortical Dynamics: A Band-Stratified Metriplectic Port-Hamiltonian Neural Network Learned from Brain-Computer-Interface EEG
Dibakar Sigdel
We present a physics-inspired classical digital twin of
brain-computer- interface (BCI) data: a graph neural network constrained to a band-stratified, metriplectic port-
Hamiltonian form, with parameters learned from scalp
EEG recorded during rest and motor imagery. The port-
Hamiltonian structure is a modelling choice - it buys passivity, a certified steady-state power balance, and a clean separation of storage, routing and dissipation - not a claim about what the
brain is. The state pairs each channel's instantaneous phase with its angular frequency, and stored energy decomposes over the five canonical frequency bands. A phase-locking prior measured from the same recordings gates the learned connectome, and a metriplectic formulation places the twin at a non-
equilibrium steady state sustained by a metabolic port. Fitted to $1{,}109{,}250$ phasor samples from the PhysioNet
EEG Motor Movement/Imagery database under a leakage-free split, the twin reaches a held-out reconstruction error of $1.30\times10^{-4}$. Scored free-running against invariants it did not author, the verdict is mixed: it reproduces near-critical avalanche branching ($\sigma\approx1$) but not the aperiodic $1/f$ slope or the long-range temporal correlations of the recordings. Skew-
symmetry and non-negative dissipation hold by construction rather than by penalty, making the twin a structure-preserving substrate on which closed-loop neuromodulation can be designed and tested.
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Artificial Intelligence · Cross-listed from cs.SE
Mitigating LLM Sycophancy in Code Smell Detection Using Evidence-Guided Reasoning Prompts
Istiaq Ahmed Fahad, Kamruzzaman Asif and Md. Nurul Ahad Tawhid
Large
Language Models (LLMs) are increasingly used for code smell detection tasks due to their ability to interpret program semantics. However, their reliability in this context remains poorly explored, particularly under varying
prompt conditions where model predictions may be influenced by external cues rather than code characteristics. One such limitation is sycophancy bias, where models tend to align their outputs with user-provided assumptions instead of performing objective analysis. In this paper, we present the first systematic empirical study of sycophancy bias in LLM-based code smell detection. Using the MLCQ
dataset, we evaluate how different
prompt framings like confirmation bias, contradictory hints, and false premises affect model predictions. Our results show that LLMs are highly sensitive to
prompt variations, with Decision Flip Rates reaching up to 72% and False
Alignment Rates exceeding 90%, indicating substantial instability and agreement with misleading
prompts. To address this issue, we propose Evidence-Guided Debiasing Prompting (EGDP), a structured prompting strategy that enforces evidence-first
reasoning. EGDP reduces decision instability and improves robustness, lowering Decision Flip Rates to as low as 12% and False
Alignment Rates to as low as 21%, while increasing reliance on structurally grounded evidence. Our findings demonstrate that sycophancy bias poses a critical threat to the reliability of LLM-based code smell detection, and that evidence-guided
reasoning provides an effective and generalizable mitigation approach.
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Artificial Intelligence · Cross-listed from cs.HC
Spatula: Exploring On-Demand In-Situ Interfaces and Interaction for Attribute Control
Boyu Li, Linjie Qiu, Lin-Ping Yuan et al. (+4 more)
Controlling attributes is a critical step toward achieving the final creative outcome, yet current approaches fall short in supporting users in the iterative refinement of generative content. We propose Spatula, a proof-of-concept system that generates on-demand, in-situ attribute control interfaces and interactions for creating motion graphics. Building on a technical probe that automatically analyzes animation context and generates corresponding attributes and UI, we frame attribute control as an explorable landscape and explore the attribute control space along four key dimensions: Discoverability, Resolution, Scope, and Expandability. Findings from a user study (N=12) show that our system provides intuitive and convenient interactions while supporting diverse needs for fine-grained parameter control. Furthermore, our applications demonstrate that the plug-and-play design generalizes to other domains, such as web design and 3D modeling.
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All authors Boyu Li, Linjie Qiu, Lin-Ping Yuan, Duotun Wang, Yue Jiang, Zeyu Wang, Hongbo Fu
Artificial Intelligence · Cross-listed from cs.CR
Large Language Models in Misinformation Ecosystems: Misuse, Defense, and Vulnerability
Lingwei Wei, Dou Hu, Wei Zhou et al. (+2 more)
Large
language models (LLMs) have transformed misinformation from a primarily content-centric problem into a broader ecosystem-level security challenge. When misused, LLMs create risks beyond false content generation, enabling attacks on the social contexts, evidence sources,
retrieval corpora, and verification workflows that misinformation defense depends on. In this paper, we introduce a role-layer framework to unify these risks and defenses. The role dimension characterizes LLMs as attackers, defenders, and vulnerable components of verification systems, while the layer dimension covers content, social contexts, evidence environments, and verification workflows. Guided by this framework, we organize LLM-enabled attacks, investigate LLM-based detection and verification methods, analyze vulnerabilities in LLM-centric detection paradigms, and discuss existing countermeasures against LLM-enabled attacks. Building on this synthesis, we identify three key open challenges: moving from static detection accuracy to budgeted ecosystem-level risk evaluation, hardening LLM-centered verification pipelines against
adversarial manipulation, and deploying auditable human-in-the-loop verification systems for trustworthy real-world misinformation defense.
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All authors Lingwei Wei, Dou Hu, Wei Zhou, Songlin Hu, Philip S. Yu
Artificial Intelligence · Cross-listed from cs.CV
SynthDocBench: Controlled Benchmark for Long-Context Visual Document Understanding
Abhigya Verma, Khyati Mahajan, Amit Kumar Saha et al. (+4 more)
Vision language models (VLMs) have achieved strong performance on visual document understanding
benchmarks such as DocVQA, ChartQA, and MMLongBench-Doc. However, real-world documents combine multiple factors such as length, layout complexity, modality, and question difficulty, which makes it difficult to attribute model failures to specific causes. We introduce SynthDocBench, a fully synthetic
benchmark for long-context visual document understanding that systematically controls factors including document length, layout structure, modality composition, and question type. The
benchmark is constructed using a combinatorial design, each factor is varied independently across generated documents, enabling controlled analysis of model behavior. Documents are generated end to end using an LLM pipeline across six layout archetypes, with a 40 percent random override to prevent models from exploiting spurious correlations. Additionally, SynthDocBench spans long-context documents with substantially greater length and structural diversity than existing
benchmarks. Evaluating seven frontier VLMs, we uncover three failure modes that existing
benchmarks cannot surface: sharp degradation with document length, a systematic positional sensitivity in which the middle third of a document is hardest for five of six models and five of six models show a negative Early-to-Late trend (steepest decline: 8.3 percentage points), and breakdown of chart comprehension in long-document settings. These results suggest that current models may be overfitting to
benchmark artifacts rather than achieving robust long-context visual document understanding.
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All authors Abhigya Verma, Khyati Mahajan, Amit Kumar Saha, Shruthan Radhakrishna, Sagar Davasam, Vikas Yadav, Sai Rajeswar Mudumba
Artificial Intelligence · Cross-listed from cs.CL
A Stepwise Questioning Expert-Editor Multi-Agent Framework for Long-Document Summarization
Lingyun Shen and Xuejia Guo
Although large
language models (LLMs) have shown promising potential in news summarization tasks, their performance on long-document summarization remains challenging as their length often exceeds the input limits. As the
agent investment, which provide possibility to improve the inherent capabilities of LLMs. To enhance the effectiveness of long-document summarization based on LLMs, this paper proposes an expert-editor stepwise questioning multi-
agent method, in which the expert and the editor guide another
agent to refine the summary by posing questions on different aspects of the content and providing targeted clues for revision. We conducted experiments on two representative long-document scientific
datasets and evaluated the results through widely recognized automatic metrics. The results demonstrated the effectiveness of our method.
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Artificial Intelligence · Cross-listed from cond-mat.mtrl-sci
The evolution of AI from image interpretation toward scientific inference in nanoparticle electron microscopy
Evropi Toulkeridou, Jiafei Li, Leonardo Lari et al. (+1 more)
Artificial intelligence (AI) is transforming electron microscopy by enabling quantitative analysis of increasingly large and complex
datasets for nanoparticle characterization. Recent advances in machine learning (ML) and deep learning (DL) have expanded microscopy from a descriptive imaging technique into a data-driven platform for structural interpretation, dynamic analysis, and scientific inference. This review examines AI methodologies for nanoparticle electron microscopy, focusing on transmission electron microscopy (TEM), high-resolution transmission electron microscopy (HRTEM), scanning transmission electron microscopy (STEM), and in situ TEM. The discussion is organized around the principal challenges in nanoparticle characterization, including particle detection,
segmentation, morphology quantification, atomic-resolution restoration, defect identification, two-dimensional-to-three-dimensional structural inference, and analysis of dynamic processes in situ. We review computational approaches from conventional ML and convolutional neural networks to
transformer architectures, self-supervised learning, foundation models,
multimodal AI, and physics-informed learning. We further discuss integrating microscopy data with simulations, metadata, and autonomous experimentation to relate nanoparticle structure, dynamics, synthesis conditions, and functional properties. The advantages, limitations, benchmarking, and data requirements of current methodologies are critically assessed. Finally, emerging opportunities for foundation models, AI-guided microscopy, closed-loop experimentation, and autonomous materials discovery are discussed. By integrating advances across computer
vision, materials informatics, and electron microscopy, this review highlights the role of AI in next-generation nanoparticle characterization and accelerated materials discovery.
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All authors Evropi Toulkeridou, Jiafei Li, Leonardo Lari, Panagiotis Grammatikopoulos
Artificial Intelligence · Cross-listed from cs.CL
Structured Thoughts For Improved Reasoning And Context Pruning
Zain Sarwar, Supriyo Chakraborty, Berkcan Kapusuzoglu et al. (+5 more)
Large
language models (LLMs) excel at generating long chains of thought, but long
reasoning traces are often verbose and
memory-inefficient. In this work, we introduce Structured Thoughts, a framework that organizes
reasoning into alternating <try> and <outcome> blocks: <try> captures exploratory scratch work, while <outcome> contains the distilled conclusion of that step. We construct a
dataset of structured thoughts by segmenting
reasoning traces into <try> blocks and prompting an LLM to summarize each step into its corresponding <outcome>. Fine-tuning pretrained foundation models on this reformatted data produces models that adopt the structured
reasoning style, leading to performance gains of up to 8.08% on
reasoning benchmarks compared to standard SFT. The explicit structure also enables context
pruning: after each <try>/<outcome> pair, the <try> can be pruned, allowing the model to retain conclusions without keeping the full scratch work in the context. A proof-of-concept
pruning implementation achieves an average of 85%
memory / context savings with an 8.67% performance drop across mathematical tasks.
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All authors Zain Sarwar, Supriyo Chakraborty, Berkcan Kapusuzoglu, Chia-Hsuan Lee, Anirban Das, Stephen Rawls, Kartik Balasubramaniam, Sambit Sahu
Artificial Intelligence · Cross-listed from cs.CV
ABot-N1: Toward a General Visual Language Navigation Foundation Model
Ruiyan Gong, Yingnan Guo, Junjun Hu et al. (+43 more)
Visual
Language Navigation foundation models aim to unify deep
reasoning for grounded spatial decisions with broad versatility for diverse
embodied tasks. Current approaches typically achieve this integration via monolithic policies that map observations directly to actions, yet they often suffer from coordinate drift and poor handling of long-tail semantics. Furthermore, these black-box mappings lack interpretability, hindering the simultaneous achievement of generality, robustness, and transparency. We present ABot-N1, a step toward a general Visual
Language Navigation foundation model, that addresses these challenges by decoupling cognition from control via a slow-fast architecture guided by dual visual-
language signals. More specifically, a slow
vision-
language reasoner performs explicit Chain-of-Thought
reasoning while producing a pixel goal. This compact set of image-space anchor points serves as a universal interface for diverse tasks, including point-goal, object-goal, poi-goal, instruction-following, and person-following. Subsequently, a fast action expert leverages both the textual cues and the pixel guidance to generate continuous waypoints at the native control frequency. By bridging high-level intents and low-level control through pixel-grounded anchors paired with explicit linguistic traces, our approach ensures robust, generalizable, and interpretable
navigation across simulation and real-world
benchmarks. ABot-N1 establishes new state-of-the-art records, delivering massive gains specifically in urban-scale
navigation: boosting POI arrival by 35.0% (to 77.3%) and achieving 95.4%/92.9% SR in complex indoor and outdoor scenes. It also maintains superior robustness across object-reaching, person-following, and instruction-following tasks. New Point-Goal/POI-Goal
benchmarks are released as open source to advance the field of urban-scale
navigation.
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All authors Ruiyan Gong, Yingnan Guo, Junjun Hu, Jintao Kong, Xiaoxu Leng, Tianlun Li, Weize Li, Fei Liu, Zhicheng Liu, Jia Lu, Minghua Luo, Chenlin Ming, Yanfen Shen, Jiyue Tao, Zhengbo Wang, Mingyang Yin, Minqi Gu, Zihao Guan, Wei Guo, Guoqing Liu, Huachong Pang, Menglin Yang, Zeqian Ye, Xiaoxiao Geng, Zhining Gu, Honglin Han, Di Jing, Hongyu Pan, Mingchao Sun, Kuan Yang, Jianfang Zhang, Yanghong Chen, Ye He, Wei Mei, Jiahao Shi, Xiangpo Yang, Yanqing Zhu, Yang Cai, Jingjing Ma, Shihui Su, Zixiao Tang, Linbo Zheng, Zedong Chu, Xiaolong Wu, Wenbin Tang, Mu Xu
Artificial Intelligence · Cross-listed from cs.RO
VINE: Taming Generative Control Policies for Reinforcement Learning
Rushuai Yang, Zhuo Han, Houlin Li et al. (+10 more)
Flow-matching policies have emerged as an effective policy parameterization for
robot learning. They iteratively generate actions from noise, enabling highly expressive modeling of complex and
multimodal action distributions. However, prior works observed that scaling these policies with value-gradient reinforcement learning (RL) often leads to training instability. Existing methods attribute this instability to iterative generation and therefore avoid end-to-end value-gradient optimization by sacrificing iterative generation, high expressiveness, or value-gradient optimization. Contrary to prior belief, we show the instability does not stem from iterative generation itself, but from the vanilla sampling strategy originally designed for behavior cloning, which becomes brittle under value-gradient RL. Motivated by this insight, we propose VINE, an RL-oriented sampling method that enables stable end-to-end value-gradient optimization for flow-matching policies. Instead of following a single flow trajectory, VINE reconstructs a new interpolation state at every denoising step, creating a stable differentiable path for value-gradient propagation while remaining compatible with the original flow-matching denoising process. As a result, VINE preserves the expressiveness and iterative generation of flow-matching without sacrificing end-to-end value-gradient optimization. Despite performing end-to-end backpropagation through all ten denoising steps, VINE achieves stable policy improvement and consistently outperforms state-of-the-art RL methods on the OGBench offline RL
benchmark and real-world robotic manipulation task.
Videos are available on our website: https://agibottech.github.io/vine.
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All authors Rushuai Yang, Zhuo Han, Houlin Li, Hecheng Wang, Zhichao Wu, Rui Zhang, Zhaowei Zhang, Zihong Chen, Xiaohan Yan, Chiming Liu, Yi Chen, Wei Shan, Maoqing Yao
Artificial Intelligence
Co4ICF: Co-evolving Physics-Informed Surrogate and RL-based Pulse Optimizer for Inertial Confinement Fusion
Jiatong Zhao, Tengyue Zhang, Yuhan Wang et al. (+2 more)
Offline-trained surrogates for Inertial Confinement Fusion (ICF) suffer a well-known failure mode that iterative optimizers drive inputs into out-of-distribution (OOD) regions where predictions become unreliable. Here we present Co4ICF, a co-evolving framework that couples a physics-informed surrogate with a PPO-based pulse optimizer. The surrogate is iteratively fine-tuned on policy-induced trajectories, correcting extrapolation errors as the optimizer shifts the input distribution; the optimizer queries this evolving surrogate as a fast environment. In the 1D MULTI environment, Co4ICF achieves 146.1% normalized yield based on current
laser design baseline; as a post-hoc cross-fidelity check, the optimized pulse further attains 246.9% normalized yield when directly evaluated in 2D-MULTI without any 2D training or fine-tuning. Budget-matched ablations support that the gains are not explained solely by additional simulation data and are consistent with the co-evolving mechanism playing a key role. We release a large-scale MULTI-IFE simulation
dataset to support future benchmarking.
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All authors Jiatong Zhao, Tengyue Zhang, Yuhan Wang, Fuyuan Wu, Junchi Yan
Artificial Intelligence · Cross-listed from cs.LG
A Hyperbolic Neural Closure for M1 Radiation Transfer
Bongseok Kim, Jiahao Zhang, Johannes Krotz et al. (+3 more)
In radiation transfer simulations, an M1 method achieves substantial computational savings by replacing the full angular transport equation with a low-order moment system. Because this reduced system is not closed, a closure model is required to represent the unknown higher-order moments using lower-order moments. While machine learning (ML)-based closures can improve accuracy beyond classical analytic closures, unconstrained learned closures may produce non-real characteristic speeds and consequently cause numerical solver breakdown. To guarantee real
eigenvalues of the Jacobian associated with ML closures, we propose a
hyperbolic neural closure for the M1 radiative transfer system. Rather than directly predicting closure terms, we parameterize the Jacobian through two neural networks: (i) a symmetric
matrix network and (ii) a strictly convex
entropy network whose Hessian defines a positive definite symmetrizer. These components are combined to yield a Jacobian that is similar to a symmetric
matrix, thereby ensuring real
eigenvalues. The closure is then reconstructed by numerical integration of the learned Jacobian field along a prescribed integration path. Numerical experiments show that the proposed closure not only achieves higher closure accuracy than classical analytic closures, but also improves solution accuracy and remains stable in discontinuous Galerkin simulations for radiative transfer problems.
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All authors Bongseok Kim, Jiahao Zhang, Johannes Krotz, Dinshaw Balsara, Ryan McClarren, Guang Lin
Artificial Intelligence · Cross-listed from cs.CV
Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
Giang Nguyen, Raghav Mehta, Emma A.M. Stanley et al. (+4 more)
Foundation models are increasingly used as image feature extractors for mammography, but their robustness under external domain shift remains unclear. We
benchmark 15 foundation-model backbones across breast density, BI-RADS severity, and cancer status using a unified frozen-backbone linear-probe protocol, training on 3 source
datasets and evaluating on 12 task-compatible out-of-distribution (OOD)
datasets after label harmonization. Mammography-specific
vision-
language models (Mammo-FM and MaMA) provide the strongest mean OOD performance, but robustness is not explained by mammography exposure alone. DINOv3 remains a competitive
vision-only baseline, and mammography-adapted pretraining does not consistently improve generalization.
Dataset-level analysis further shows that even leading models show heterogeneous performance across
datasets. Feature-space inspection reveals that useful representations can preserve clinical signal while retaining
dataset and acquisition structure. These findings highlight
dataset-level OOD evaluation as a central criterion for assessing mammography representations. Our code is publicly available: https://github.com/biomedia-mira/mammo-ood.
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All authors Giang Nguyen, Raghav Mehta, Emma A.M. Stanley, Tian Xia, Thi Hao Nguyen, Hieu Pham, Ben Glocker
Artificial Intelligence · Cross-listed from cs.CV
GRC-ProbNet: Uncertainty-aware Feature Extraction for Cardiovascular Disease Classification
Yash Shah, Omar Todd, Philipp Seeböck et al. (+3 more)
The automatic detection and classification of cardiovascular disease (CVD) from computed
tomography (CT) images plays an important role in clinical practice. Recently, a hybrid pipeline (GRC-Net) for CVD classification was proposed, which leverages a deep-learning-based
segmentation and registration method to extract radiomic and
geometric features. However, GRC-Net relies on a deterministic
segmentation mask, without considering the inherent ambiguity associated with cardiac anatomy. In this paper, we propose GRC-ProbNet, which takes advantage of a deep ensemble to produce multiple
segmentation masks for a given input. From these masks, we extract multiple uncertainty features. We analyze these uncertainty features for both their correlation with
segmentation error and their propagation effects on downstream CVD classification performance. Our experiments on the publicly available MM-WHS and ASOCA
datasets show that the uncertainty measure that best reflects
segmentation quality is not necessarily the one that provides the strongest signal for downstream CVD classification. Overall, our results demonstrate that GRC-ProbNet utilizing uncertainty features substantially improves CVD classification AUROC (92.92\) compared to the baseline GRC-Net model (91.25%). Our code is publicly available: https://github.com/biomedia-mira/GRC-ProbNet.
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All authors Yash Shah, Omar Todd, Philipp Seeböck, Georg Langs, Ben Glocker, Raghav Mehta
Artificial Intelligence
ABot-AgentOS: A General Robotic Agent OS with Lifelong Multi-modal Memory
Jiayi Tian, Shiao Liu, Yuting Xu et al. (+30 more)
Recent VLM and VLA systems have improved robotic perception and action prediction, yet long-horizon
embodied agents still require a general runtime layer for
reasoning,
memory, tool use, verification, and cross-embodiment execution. We present ABot-AgentOS, a general robotic
Agent Operating System that sits above low-level controllers and provides a deliberative
agent layer for scene-conditioned planning, context-isolated skill execution, multi-stage verification, multi-modal
memory, and edge-cloud collaboration. To evaluate such systems, we introduce EmbodiedWorldBench, an executable
benchmark with 16 indoor, outdoor, and hybrid scenes, four difficulty levels, and over 200 tasks involving
navigation, object search, NPC dialogue, dynamic events, and trace-grounded scoring. ABot-AgentOS further introduces Universal Multi-modal Graph
Memory, a persistent source-grounded substrate that converts dialogue, visual observations, spatial context, temporal relations, and task traces into typed nodes and edges. A failure-driven self-
evolution loop converts diagnosed
memory failures into gated runtime evo-assets that are promoted only to later evaluation splits, preventing current-split ground-truth leakage while enabling continual improvement. On an initial EmbodiedWorldBench subset, ABot-AgentOS improves over a single-controller baseline in both task success and goal completion. Across
memory benchmarks, ABot-AgentOS Static achieves 87.5 on LoCoMo, 59.9 on OpenEQA EM-EQA, 88.6 on Mem-Gallery, and 76.5 Acc@All on NExT-QA; self-
evolution further improves LoCoMo to 88.7, OpenEQA to 60.4, and Mem-Gallery to 89.0. These results suggest that a general
Agent OS layer can improve long-horizon
embodied execution while providing persistent, auditable
memory for continual interaction.
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All authors Jiayi Tian, Shiao Liu, Yuting Xu, Jia Lu, Zihao Guan, Honglin Han, Di Yang, Minqi Gu, Yifei Qian, Tianlin Zhang, Yanqing Zhu, Zeqian Ye, Menglin Yang, Fei Wang, Xu Hu, Xiuxian Li, Wei Zhang, Shihui Su, Yiyan Ji, Jingbo Wang, Ziteng Feng, Jiaheng Liu, Zhaoxiang Zhang, Xiaolong Wu, Zixiao Tang, Zhining Gu, Yang Cai, Linbo Zheng, Jingjing Ma, Mingyang Yin, Zedong Chu, Wenbin Tang, Mu Xu
Artificial Intelligence · Cross-listed from stat.AP
From Stochastic to Stable: Rank Stability and Structural Sufficiency in AI Visibility Measurement
Ronald Sielinski
AI visibility measurement is comparative: practitioners want to know which domains generative search engines cite most often and whether observed differences are large enough to support decisions. Yet the industry lacks a principled way to determine whether enough data has been collected. Collection budgets vary widely across studies and platforms, and conclusions are often drawn from rankings whose stability and precision are unknown. We introduce a sequential convergence framework based on two complementary criteria: rank stability evaluates whether the rank-correlation trajectory has reached a structural plateau, while structural sufficiency evaluates whether the spread of citation shares among established domains – those whose confidence intervals exclude zero – exceeds the uncertainty of those estimates. Together, these criteria distinguish rankings that have merely stabilized from those sufficiently resolved to support inference. Both are derived from regularities in the observed citation distribution, including its rank structure, uncertainty profile, and the boundary between observed and established domains. The framework retains a small number of structural constants but requires no externally specified query count, correlation target, or confidence-interval width target; stopping is driven by observed measurement uncertainty and remains robust across a range of sufficiency thresholds. Applied across 30 platform-topic combinations spanning Gemini, SearchGPT, and Perplexity, the framework adapts to platform- and topic-specific citation distributions. Results show that no fixed collection budget can be justified across contexts and that convergence can instead be evaluated from the structure of the observed distribution. The framework provides a practical basis for determining when AI visibility measurements are ready to support comparative analysis.
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Artificial Intelligence
Comparing Socio-technical Design Principles with Guidelines for Human-centered AI
Thomas Herrmann
Human-centered AI (HCAI) refers to guidelines or principles that aim on ethi-cally oriented design of systems. We compare HCAI- guidelines with princi-ples of socio-technical systems that emerged in the context of conventional in-formation technology. The comparison leads to a revision of socio-technical heuristics by including aspects of AI-usage. The comparison reveals that con-tinuous
evolution is a basic characteristic of socio-technical systems, and that human oversight or interventions and the subsequent appropriation of AI-systems lead to continuous adaptation and re-design of the systems, if autono-my is collaboratively exercised. From a socio-technical point of view, the cru-cial requirement of transparency has not only to be fulfilled with technical fea-tures, but also by contributions of the whole system including human actors. It will be promising for using AI, if not only technical features, but organization-al and social practices are socio-technically designed in a way that compen-sates shortcomings of AI.
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Artificial Intelligence · Cross-listed from cs.CL
Neutralizing Structural Inequality in the Nigerian FinTech Sector
Muhammad Abdullahi Said
Algorithmic decision systems in financial services often rely on data proxies that inadvertently encode structural inequalities. This paper introduces a hierarchical human-AI triage model for Point of Sale fraud detection in the Nigerian FinTech sector. Adopting a We Are All Equal worldview, we address the challenge of discrimination laundering, wherein the system misinterprets infrastructure related aleatoric noise such as rural network timeouts as fraudulent intent. We implement a three-tier routing policy utilizing a calibrated ensemble model as a primary filter. The policy routes transactions characterized by epistemic uncertainty such as cold start new accounts to specialist analysts while reserving high stakes cases for a senior supervisor. To manage finite human capacity, we utilize a dynamic shadow price to ration human
attention and implement a random audit mechanism to prevent human skill atrophy. Our experimental results demonstrate a statistically significant 1.88% complementarity gap and a 24.79% percentage point gain in fraud recall over an autonomous baseline. Crucially, the model reduces the regional performance gap from 19.43 to 2.88 percentage points, neutralizing structural bias. Hierarchical collaboration provides a robust mechanism for substantive equality of opportunity, ensuring that rural accounts are not excluded from the digital economy due to environmental brute luck.
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Artificial Intelligence · Cross-listed from cs.CL
Polarization Detection: A Hybrid Approach with AfroXLMR-Social and DeBERTa for Low- and High-Resource Settings
Muhammad Abdullahi Said
The rapid proliferation of online polarization threatens social cohesion, necessitating robust automated detection systems that operate effectively across diverse linguistic contexts. This paper presents our system description for the POLAR Shared Task 2026, focusing on the detection and characterization of polarized discourse in English and Hausa. We propose a hybrid modeling strategy: for English binary detection, we leverage the monolingual strength of DeBERTa, while for Hausa and all fine-grained subtasks (Types and Manifestations), we utilize AfroXLMR-Social. This domain-adapted
multilingual model proved critical for capturing the nuances of polarization in social media text. To further address computational constraints and data scarcity, we implement Low-Rank Adaptation (LoRA) and textual data augmentation via nlpaug. We report competitive results across all three subtasks, demonstrating that model selection tailored to specific subtask requirements yields the best balance of performance.
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