Graphics
FootprintRAG: Visual Analytics for Evidence Context Refinement in RAG-based Scientific Literature Exploration
Xingyu Liu, Yu Dong, Qizhen Yu et al. (+7 more)
Retrieval-Augmented Generation (RAG) is increasingly used to ground large
language model (LLM) outputs in scientific literature. However, in open-ended literature exploration, the evidence context used for generation is often produced through hidden
retrieval, reranking, assessment, and filtering steps. Users may receive
retrieval summaries without knowing how the system constructed the evidence context, which evidence units were retained or discarded, or whether potentially useful evidence was excluded before synthesis. We present FootprintRAG, an LLM-
agent-powered visual analytics system for evidence context refinement in RAG-based scientific literature exploration. The core idea is to treat the RAG evidence context as an explicit, inspectable, and revisable analytical object before generation. FootprintRAG parses scientific literature into text and figure evidence units, expands an initial query into parallel query variants, retrieves and assesses evidence across iterative rounds, and surfaces ERS-ranked supplementary candidates from the corpus-level evidence space. Through coordinated views, the system connects
retrieval trajectories, evidence-state revision, and provenance-aware summary generation into a user-steerable workflow. We evaluate FootprintRAG through two case studies, a user study, and a workflow-level comparison with representative RAG systems. The results show that FootprintRAG helps users compare
retrieval directions, revise candidate evidence, recover potentially overlooked evidence, and trace generated summaries back to supporting evidence units. FootprintRAG is available at https://github.com/meteorshowering/FootprintRAGVA.git.
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All authors Xingyu Liu, Yu Dong, Qizhen Yu, Shiyu Cheng, Zhe Wang, Guan Li, Guihua Shan, Dong Tian, Christy Jie Liang, Quang Vinh Nguyen
Robotics
WorldContact: A Contact-Centric World Model for Scalable Robot Learning
Caoliwen Wang, Mengdi Wang, Heng Zhang et al. (+7 more)
Adapting
robots to new objects and tasks requires interaction experience that can be costly to obtain. We present WorldContact, a contact-centric world model for deformable-object manipulation, constructed from a limited set of high-quality trajectories to generate additional training data efficiently. It predicts object dynamics using larger time steps than the source numerical simulator, which requires small integration steps to resolve rapid motion and prevent interpenetration. We evaluate WorldContact across 16 shopping-bag manipulation tasks. State-rollout measurements on a single H100 GPU show a $10\times$ speedup over the source simulator, excluding rendering and disk I/O. We use the generated data to fine-tune an existing
vision-
language-action policy and deploy it directly on a real
robot. In bag lifting, the same policy achieves 65% single-attempt success when fine-tuned on source simulation data alone, compared with 95% when fine-tuned on the
dataset expanded with WorldContact. These results support efficient data generation with WorldContact for
robot policy adaptation.
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All authors Caoliwen Wang, Mengdi Wang, Heng Zhang, Shixun Huang, Siyuan Chen, Chao Liu, Anpei Chen, Zhendong Wang, Peter Yichen Chen, Huamin Wang
Programming Languages
Theories of Mind as Domain-Specific Languages of Thought
Kartik Chandra, Jonathan Ragan-Kelley, Joshua B. Tenenbaum et al. (+1 more)
What kind of thing is a “theory of mind”? We propose to formalize theories of mind as domain-specialized programming
languages, which can be used to reason about the mental states of other
agents. Our proposal builds on the longstanding idea that thinking is akin to programming in an internal “
language of thought.” But rather than positing a single general-purpose
language for all of thought, we posit a collection of domain-specific
languages of thought, whose respective syntaxes and semantices encode the theories of various domains. We make this idea concrete in the domain of intuitive psychology using a real-world programming
language called memo, which is specialized for theory-of-mind
reasoning via domain-specialized syntactic constructs like “knows,” “wants,” and “thinks.” In our view, using one's theory-of-mind to think about a social situation is analogous to using memo to write a program that models that situation. Through a series of worked examples, we show how formally representing a theory of mind as a memo-like programming
language can lend insight into a variety of theoretical issues in theory-of-mind research: (1) how a single, fixed intuitive theory can be used to reason about social situations with unbounded structural variation; (2) how a modular, domain-specialized, and informationally-encapsulated system for social cognition might interface with external world knowledge, as well as other cognitive systems like intuitive physics; and (3) how different theories of mind, such as those of children and adults, differ, what exactly is gained as theories grow over the course of development, and how that growth might occur.
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All authors Kartik Chandra, Jonathan Ragan-Kelley, Joshua B. Tenenbaum, Rebecca Saxe
Computation and Language
Full-Duplex Speech Models Take the Floor When Asked, Not When Needed
Linkai Peng, Baorian Nuchged, Kaiqi Fu et al. (+1 more)
Full-duplex
speech models listen and speak at once, promising always-on assistants. Yet they must also decide when they should speak. Human listeners speak when addressed or when the speaker stops, but also self-select to correct a false claim, supply a missing word, or warn of danger. We ask whether full-duplex models do the same. To separate the reason to speak from the opportunity, we construct context-matched English monologues in which only the trigger utterance varies within a topic, define 10 conditions from turn-allocation rules, and compress inter-word pauses to limit opportunities created by silence. Across five model families, being addressed and silence are far more reliable triggers than false facts or hazards. Frame-level text-
token probabilities in Moshi and PersonaPlex are lower for false facts than for Neutral when averaged over the first 2\,s after trigger end. Pauses or permission to interrupt do not close this gap either. Given the floor, Moshi and PersonaPlex answer most direct questions, yet the proportion of non-empty false-fact replies that challenge the claim is only .14–.15, and the proportion of hazard replies that warn of danger is .04–.07. This paper thus identifies a gap in both
speech initiation and response content. Closing it requires genuine content understanding and intervention decisions grounded in it.
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All authors Linkai Peng, Baorian Nuchged, Kaiqi Fu, Yuyang Yao
Game Theory
Condorcet-type properties of the linear ordering problem with ties
Daichi Kawashima and Noriyoshi Sukegawa
The Kemeny rule aggregates multiple strict rankings into a single strict ranking that minimizes the sum of its distances from the input rankings. The resulting optimization problem, called the Kemeny problem (KP), is a special case of the linear ordering problem (LOP). The Kemeny rule satisfies several desirable properties in social choice theory, including the extended Condorcet criterion (XCC). Ando et al. strengthened this result by introducing the strong Condorcet criterion (SCC) and showing that it holds for every optimal solution to an arbitrary LOP instance. Yoo and Escobedo extended the Kemeny rule to rankings with ties and showed that the resulting rule satisfies the non-strict extended Condorcet criterion (NXCC). This criterion gives a condition under which one candidate must be ranked strictly above another in every optimal solution. In this paper, we introduce the non-strict strong Condorcet criterion (NSCC), a counterpart of the SCC for rankings with ties, and show that it holds for every optimal solution to an arbitrary instance of the linear ordering problem with ties (LOPT). We also establish a complementary structural property that gives conditions under which two candidates must be tied in every optimal solution to an arbitrary LOPT instance.
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Computer Vision
Selective Cotton Boll Localization for Robotic Harvesting: Evaluation of Deep Learning Vision Models Under Field Conditions
Thevathayarajh Thayananthan, Xin Zhang, Isuru Laddusinghe Badu et al. (+6 more)
This study developed and evaluated a deep-learning-based perception framework for selective robotic cotton picking. The
dataset contained 1,008 annotated field images collected using three cameras under varying natural lighting and weather conditions. Object-detection models from the YOLOv8 through YOLOv13 families were evaluated using their default configurations, while
segmentation performance was assessed using YOLOv8-seg, YOLOv11-seg, YOLOv12-seg, the Segment Anything Model (SAM), SAMv2.1, FastSAM, and Grounded-SAM with the Recognize Anything Model (RAM). Among the detection models, GELAN-s achieved the most favorable balance between mean average precision (mAP) and inference speed, obtaining an mAP of 86.1%, precision of 81.6%, recall of 76.6%, and an F1-score of 79.0%, with an average inference time of 42.3 ms per image. Among the direct
segmentation models, YOLOv12-m-seg provided the most favorable balance between AP@0.5 and FPS, achieving a
segmentation AP@0.5 of 83.7% with an inference time of 20.4 ms per image. In the detection-prompted
segmentation approach, bounding-box
prompts generated by GELAN-s improved the localization of cotton bolls for SAM and SAMv2.1, while SAMv2.1 Tiny consistently outperformed FastSAM and Grounded-SAM with RAM. In the area-based evaluation against manually annotated
segmentation masks, YOLOv12-m-seg achieved an $R^2$ value of 0.966, compared with 0.860 for GELAN-s + SAMv2.1 Tiny. Field experiments conducted using a UR5e robotic manipulator, a custom end-effector, and a ZED2i stereo camera further validated the effectiveness of the YOLOv12-m-seg model for real-time cotton boll detection,
segmentation, and selective picking under varying confidence levels. These results demonstrate that YOLOv12-m-seg provides an efficient perception model for robotic cotton harvesting and has strong potential for field deployment.
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All authors Thevathayarajh Thayananthan, Xin Zhang, Isuru Laddusinghe Badu, Jonathan Harjono, Glen C. Rains, Beiwen Li, Leonardo M. Bastos, Nuwan K. Wijewardane, Vitor S. Martins
Computation and Language
Form Over Content In Gradient-Based Data Attribution Methods
Sunwoo Kim, Seokwon Jung, Sohyung Kim et al. (+2 more)
Data attribution methods using gradient similarity are widely used to analyze and select training data for large
language models, but what gradient similarity actually measures is debated. Some interpret it as identifying task-relevant skills, while other work reports that surface form is the main factor. We resolve this debate for supervised fine-tuning examples by varying task and answer format independently. Specifically, we render
benchmarks in different answer formats, such that
datasets can share a task without a format or a format without a task. We find that gradient
alignment follows the answer format, as
benchmark pairs sharing an answer format align strongly (disattenuated cosine near 0.4), while same
benchmarks rendered with different answer format classes show no
alignment (near 0.0). We demonstrate that this ordering holds from the earliest pretraining checkpoints through post-training, and across model scales and families. We then analyze the released selections of LESS, a gradient-based data selection method for instruction tuning, and find that each target's selections over-represent the target's own answer format. Hence, we demonstrate that gradient-based attribution methods track format similarity more than task semantics, meaning that such methods, as well as the semantic interpretation of the gradient, should be tested on data where answer format and task vary independently for greater robustness and reliability.
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All authors Sunwoo Kim, Seokwon Jung, Sohyung Kim, Seong Joon Oh, Alice Oh
Robotics
Quantifying Mechanical Intelligence in Legged Robots with Information Theory
Zach J. Patterson
Mechanical intelligence, loosely defined as the reduction in control burden afforded by a
robot's physical form, has become a prominent concept in robotics, with instantiations in bioinspired robotics, soft robotics, robotic swarms, and many other areas. However, rigorous theoretical understanding and quantitative measures of mechanical intelligence have lagged behind the engineering systems that the community has developed. In this work, using modern legged
robots as a
benchmark and exemplar, we propose several information-theoretic metrics for quantifying mechanical intelligence. By viewing body dynamics as both a computational process and a communication channel, we show that several prior insights in legged-
robot engineering can be described using information theory, and we quantify how bits are processed by mechanical modes and across
robot coordinates. Specifically, we examine the trade-off between explicitly incorporating compliance through series-elastic actuation and using so-called proprioceptive, low-gear-ratio transmissions, and we explore how these mechanisms interact with control policies during locomotion. We develop these results on systems of increasing complexity: a simplified linear model of a
robot-leg transmission, a nonlinear single-leg simulation, and simulated quadruped
robots controlled by a learned policy while navigating challenging terrain. These results lay the groundwork for broader study of
robot mechanisms and their role in
embodied computation.
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Cryptography and Security
Red-Teaming Auto Mode: Improving Blocking Classifiers Against Malign Coding Agents
Alex Remedios, Simon Storf, Fabien Roger et al. (+1 more)
To keep coding
agents from going off the rails, production systems now review each proposed action with a blocking monitor that can reject it before it runs (Auto Mode in Claude Code, Guardian in OpenAI's Codex). Prior evaluations of such monitors largely measure robustness to accidental harm or
prompt injections from untrusted sources looking to hijack the
agent. Less understood is how they hold up when the
agent they monitor is persistently misaligned. To understand this risk, we task an
adversarial agent with evading production blocking monitors and causing catastrophic harm, e.g. by exfiltrating its own weights. We find that when instructed with high-level attack strategies,
adversarial agents can succeed through several distinct mechanisms, such as
agent-generated
prompt injection against the monitor, multi-
agent attacks, and malicious compaction. In particular we find that in 79% of trials, the
agent can use an injection attack against Auto Mode and Guardian to run arbitrary bash commands. We also find that it is possible to greatly improve Auto Mode through design changes like enhancements to tool coverage, changes in transcript formatting, and the addition of an agentic monitor stage. Even with these improvements, preventing multi-context attacks at an acceptable cost remains an open problem. By detailing our red-teaming methodology and highlighting new attack vectors, we aim to help defenders evaluate their mitigations against the possibility of persistent malign coding
agents. Code is available at https://github.com/safety-research/red-teaming-auto-mode.
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All authors Alex Remedios, Simon Storf, Fabien Roger, John Hughes
Computational Engineering
An Insurance Broker for Every Small Business: The Economics of Exceptional Care at Scale
Pierre-Alexandre Kamienny, Parthasarathi Ainampudi, Corentin Hugot et al. (+3 more)
Small-business owners need expert guidance on their own terms, across schedules,
languages, and channels, but low premiums make exceptional, continuous human service uneconomic for much of the
market. Combining public evidence, Kinro operational data, and an illustrative five-year service model, we show why traditional brokerage economics leave 35 million U.S. small businesses underserved. An AI-native brokerage can change those economics by performing and coordinating routine work continuously, while licensed professionals govern consequential exceptions and the brokerage remains accountable.
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All authors Pierre-Alexandre Kamienny, Parthasarathi Ainampudi, Corentin Hugot, Hemanth Sai Kosari, Robert Martin, Armand Bechy
Computation and Language
CliniCIRCA: A Modular LLM Framework for Constructing Longitudinal Mental Health Patient Journeys from Raw EHR Narratives
Aiwei Ivy Zhang, Nimra Ishfaq, Mohit Chandra et al. (+5 more)
In mental health care,
reasoning over patient journeys is a key task for clinicians. Yet these journeys, encompassing a longitudinal progression of biological, psychological, and social events, are often spread across disparate unstructured text narratives, making temporal recovery challenging. We present CliniCIRCA, a multi-stage LLM framework for Calendar-anchored, Imprecision-aware Reconstruction of Clinical Annals. To our knowledge, CliniCIRCA is the first to temporally classify clinical events across unstructured discharge summaries without event-level timestamps. From 14,882 MIMIC-III mental health admissions, we first construct a
benchmark of 52 discharge summaries on which CliniCIRCA produces 15,891 temporally tagged events. After correcting 629 errors based on a clinician-in-the-loop evaluation, we produce verified gold-standard labels. Finally, the corrected timelines drive a temporally grounded summarization stage that compresses each source 1.52 times into a date-grouped chronological record. We then scale the framework to generate 1,000 silver-standard timelines and evaluate them as training data. Compared with zero- and few-shot prompting, instruction tuning generally improves five open-weight models on event extraction, temporal tagging, and summarization across silver and clinician-verified evaluations.
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All authors Aiwei Ivy Zhang, Nimra Ishfaq, Mohit Chandra, Santiago Alvarez Lesmes, Adam Coscia, Khatiya Chelidze Moon, Xiaohan Ding, Munmun De Choudhury
Programming Languages
LLVM Translation Validation Automated with Large Language Models and Lean
Chunhao Liao, Hongxu Xu, Xintong Zhou et al. (+2 more)
LLVM is the cornerstone of modern compilers, but its subtle intermediate representation (IR) semantics make transformations error-prone and necessitate formal verification. Alive2, a state-of-the-art translation validator based on satisfiability modulo theories, has achieved substantial success in automating the validation of LLVM transformations. However, it still faces scalability limitations, does not support symbolic bitwidths, and offers only bounded guarantees for loops. In contrast, interactive
theorem provers such as Lean can address these cases but require substantial proof engineering. In this paper, we present Trivet, a framework combining large
language models (LLMs) and Lean for automated translation validation of LLVM transformations. Trivet generates structured proof scaffolds based on source and target functions, automatically discharges obligations amenable to deterministic
reasoning, and delegates transformationspecific obligations to LLMs. It produces refinement proofs or counterexample-based refutations, with every successful verdict checked by the Lean kernel. On 148 LLVM transformations, Trivet verifies or refutes 147, leaving one invalid case unresolved. Successful cases include 60 loop-free transformations with symbolic bitwidths, 27 cases from a restricted class of loop-containing transformations, and 10 complex valid fixed-bitwidth cases on which Alive2 times out. Compared with an unscaffolded baseline, scaffolding enables 26 additional proofs. On cases solved by both configurations, it reduces mean proof time by 75.9% and mean monetary cost by 88%.
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All authors Chunhao Liao, Hongxu Xu, Xintong Zhou, Yizhou Zhang, Chengnian Sun
Robotics
OmniCalib: Target-Free, Task-Structured Self-Calibration for Humanoid Robots
Kaixiang Lu, Haiyu Lan, Chunxiao Qiao et al. (+6 more)
Assembly, wear, and component replacement perturb the sensor extrinsics and joint zeros encoded by a
humanoid CAD model. Existing procedures calibrate one sensor pair or require external fiducials. Using only
robot-native motion and onboard sensing, we present OmniCalib, a target-free workflow that calibrates the full upper limbs—all 14 arm joint zeros and the extrinsics of both wrist and chest cameras—as well as lower limbs and the multi-camera head rig. Each module matches a
robot-native task to a parameter block, checks observability, and writes only supported corrections to the CAD model. Our depth ICP method recovers all 14 arm joint zeros and calibrates all RGB-D camera extrinsics without any calibration target. Relative to CAD, the estimated extrinsic corrections are 10.56 mm and 1.74 degrees for the left wrist, 6.33 mm and 1.25 degrees for the right wrist, and 9.81 mm and 0.929 degrees for the chest RGB-D camera. ICP point-to-plane residual is 2.09 mm. On the same injected offsets, ICP and ArUco recover all 14 joint zeros below the 0.1-degree encoder-resolution reference. On an AGIBOT A3 Ultra
humanoid, four static double-support stances recover all 12 lower-limb joint-zero offsets injected with an RMS error of 0.063 degrees. The head module combines multi-camera visual odometry with legged odometry and dynamic compensation through the live ROS transform tree. Using only planar walking, it attains a mean SO(3) error of 1.061 degrees across three sequences. The best sequence reaches 0.775 degrees, competitive with iKalibr at 0.902 degrees from rich 6-DOF excitation. Rig-relative angles repeat within 0.140 degrees. Injection recovery and held-out tests validate each observable block.
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All authors Kaixiang Lu, Haiyu Lan, Chunxiao Qiao, You Li, Enyu Li, Yehao Lu, Jiarui Yang, Peiwen Lin, Chuang Wang
Human-Computer Interaction
Value Faces: Surfacing How Self-Presentation Shifts Across Relationships
Gabriel Koo, Rayhan Rashed and Farnaz Jahanbakhsh
People present different aspects of themselves across relationships. Computational work has captured such variation in communication style. But this variation also extends to which principles people foreground or background in a particular relationship–i.e., in the values they express and how they balance them. We conceptualize these relationship-specific expressions of values as demonstrated values. To make demonstrated values visible, we introduce Value Faces, a system that analyzes a person's existing chat histories from their everyday messaging platforms using Schwartz's ten basic human values and produces separate value profiles for their different relationships. In a mixed-methods study(N=18), we find that the resulting value profiles distinguished participants' relational contexts with twice the odds of guessing, while system-inferred differences across relationships aligned with participants' perceptions of those differences. Participants used these profiles to articulate previously implicit differences in how they presented themselves, connect them to roles and changes over time, and reconsider their self-assessments.
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Robotics
Recovering Aggressively Pruned Vision-Language-Action Models with Offline Hidden-State Distillation
Chiyoung Kim, Sanghyuk Roy Choi and Minhyeok Lee
Vision-
language-action (VLA) models let
robots follow
language instructions, but their
language backbones of several billion parameters are the main obstacle to running them on
robot hardware. Structured
pruning reduces that backbone, and removing 63% of it from OpenVLA-OFT drops LIBERO-Long success from 93.2% to 0.8%. A recent approach restores such a model with supervised fine-tuning followed by reinforcement learning, which needs online rollouts and hundreds of GPU-hours. We recover most of the lost success entirely offline. Width
pruning narrows the blocks but keeps the residual stream at its original size, so teacher and student hidden states have the same shape and are matched directly, without a projector. Training against a cache built in one teacher pass lifts the 63%-reduced student to within 3.5 points of the teacher in about 8 GPU-hours. A sweep over nine ratios locates where the recovery objective starts to matter. Up to 45% reduction the two do not differ significantly on OpenVLA-OFT. Hidden-state
distillation then adds +2.1 to +4.5 points there between 63% and 87%, and +9.4 to +22.1 points on CogACT from 63% onward. At 81% on CogACT, a tripled recovery budget narrows the distilled student's gap to the teacher to 3.9 points on average, while supervised recovery stays more than 20 points below. At matched compression, width
pruning yields higher success and depth
pruning lower latency. On a 6-DoF manipulator, the distilled student at 72% reduction reaches 77.5% success against 59.5% for supervised recovery, runs 2.23x faster on-board than the teacher, and uses 62% less
memory.
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Robotics
Tele-Traversability: Rethinking Traversability for Teleoperated Ground Robots in Terrain Navigation
Lewei Feng, Qi Chen, Wenshuo Wang et al. (+6 more)
Teleoperation, a human-in-the-loop control scheme, allows a human operator to remotely command and guide a mobile
robot to navigate in off-road environments, yet fluent and user-friendly tele-
navigation requires an
alignment of traversability evaluation between human and
robot. In the teleoperation system, the human operator typically utilizes off-site incomplete and delayed feedback via a human-machine interface to make a judgment of traversability, while the
robot makes such an evaluation based on in situ onboard sensory information, which could cause divergent traversability estimation and thus generate mismatched decisions and actions. Existing approaches for traversability modeling, estimation, and prediction are mainly derived from the view of
robots, i.e.,
robot-centric, and are practically suitable for fully autonomous mobile
robots, but neglect the influence of human operators. To address this problem, this paper extends the concept of traversability from
robot-centric to human-centric by accounting for the operator's cognitive states, such as
attention, workload, and risk tolerance or awareness, termed tele-traversability. We first revisit the definitions and roles of traversability in robotics and then extend them to teleoperation settings. Finally, we highlight future trends and open challenges of tele-traversability toward human-centric teleoperation systems.
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All authors Lewei Feng, Qi Chen, Wenshuo Wang, Xianghao Meng, Minh Kieu, Haijie Guan, Jin Wu, Fuchun Sun, Junqiang Xi
Human-Computer Interaction
Learning from Success and Failure: Acquiring Adaptive Dialogue Strategies for Social Robots
Sanae Yamashita and Yuki Okafuji
Traditional dialogue systems for social
robots require both dialogue strategies and user attribute recognition, each demanding specialized expertise. However, data collection is costly in real-world deployments, and the resulting
datasets often include many failure cases. In this study, we aim to automate the acquisition of dialogue strategies by leveraging both successful and failed interactions using a
vision-
language model (VLM) and a large
language model (LLM). We propose an architecture in which user attributes, recognized by the VLM, along with dialogue history, are fed into the LLM to generate dialogue strategies tailored to specific user attributes. We extracted dialogue strategies from an interaction
dataset collected through a field experiment and evaluated their effectiveness. The results demonstrate that explicitly representing failure strategies complements success strategies and improves performance. Our findings highlight a practical pipeline for constructing and maintaining an interpretable strategy repository from in-the-wild deployment logs by recycling abundant failure interactions as reusable constraints, ultimately reducing the development cost of social
robots.
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Artificial Intelligence · Cross-listed from q-bio.GN
Large Language Model Agents for Evidence Based Genetic Disease Severity Classification
Tohid Ghasemnejad, Ahmadreza Argha, Mark Grosser et al. (+7 more)
Disease severity classification for genetic conditions is subjective and labor-intensive, creating bottlenecks in genomic screening, where commercial panels vary widely in size and overlap. We developed an autonomous AI
agent integrating
Reasoning and Acting (ReAct) with
Retrieval-Augmented Generation (RAG) to classify 10,211 Human Phenotype Ontology terms. It uses American College of Medical Genetics (ACMG)-endorsed severity guidelines and American College of Obstetricians and Gynecologists (ACOG) quality-of-life criteria to retrieve PubMed literature, generate interpretable
reasoning chains, and independently verify claims. At the phenotype level, using expert-curated cohorts, the
agent achieved 93.55% accuracy (MCC 0.9237) with 82.6% to 91.4% of claims supported by direct evidence or valid inferences. Gene-level severity was aggregated across 8,738 pairs, identifying 3,283 autosomal recessive pairs with severe or profound presentations. External validation showed 95.2% concordance with Mackenzie's Mission gene list. This system enables standardized panel design by providing reliable, automated classification supported by direct evidence.
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All authors Tohid Ghasemnejad, Ahmadreza Argha, Mark Grosser, John Wang, Min Yang, Thantrira Porntaveetus, Tony Roscioli, Nigel H. Lovell, Mahmoud Aarabi, Hamid Alinejad-Rokny
Numerical Analysis
An interior penalty nonconforming finite element method for strain gradient elasticity
Xuehai Huang and Zheqian Tang
An interior penalty nonconforming finite element method is developed for a strain gradient elasticity (SGE) model in arbitrary dimensions, using an $H^1$-nonconforming displacement element built from vector-valued quadratic
polynomials enriched by divergence-free face bubbles. Rigorous analysis establishes optimal and robust error estimates with respect to both the Lamé coefficient $\lambda$ and the size parameter $\iota$. The underlying divergence-commuting structure further leads to nonconforming finite element Stokes complexes in two and three dimensions. Numerical experiments support the predicted convergence behavior and parameter robustness, while a circular-hole
benchmark under uniaxial tension illustrates the applicability of the method to a multiply connected domain.
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Networking · Cross-listed from quant-ph
A Bi-Objective Routing Framework for Hybrid Terrestrial-Satellite Quantum Networks
Yashpreet Khambay and Nitish K. Panigrahy
Hybrid terrestrial-satellite
quantum networks combine terrestrial fiber infrastructure with free-space links to enable long-distance
entanglement distribution.
Entanglement can be routed over different combinations of terrestrial and satellite links, resulting in paths with different
entanglement generation rates (EGRs) and fidelities. Existing routing
algorithms, however, typically reduce routing to a single-objective problem by optimizing either EGR or fidelity, or by optimizing one while constraining the other, and thus do not explicitly capture the trade-off between the two. In this paper, we present a bi-objective routing framework for hybrid
quantum networks that jointly optimizes end-to-end EGR and fidelity. We formulate routing as a Pareto optimization problem and show that it possesses a special mathematical structure: the bottleneck nature of end-to-end EGR and the multiplicative nature of end-to-end fidelity allow the problem to be transformed into a MAXMIN-MINSUM bi-criterion path problem. This transformation enables the exact
polynomial-time computation of a minimal complete Pareto set using Martins' bicriterion routing
algorithm. Our results demonstrate that hybrid networks expose substantially richer Pareto frontiers than terrestrial-only networks and that the proposed framework outperforms representative single-objective routing policies by allowing applications to select paths based on their EGR and fidelity requirements.
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Machine Learning
FedFIbOS: Fisher Importance based Optimal Submodelling for Heterogeneous Federated Learning
Yasmeen Afzal, Jeremiah D. Deng and Haibo Zhang
Heterogeneous
federated learning requires clients with diverse computational capacities to collaboratively train a global model, where each client trains a capacity-constrained submodel. Existing methods select submodel parameters using heuristic importance measures—most prominently parameter magnitude—without theoretical justification for why these measures support convergence. We identify a fundamental gap: existing parameter selection criteria lack theoretical grounding in the convergence framework, partial client participation introduces additional estimation effects in the Fisher scores. We propose FedFIbOS: Fisher Importance-based Optimal Submodelling for heterogeneous
federated learning, using Fisher Information in a principled criterion derived from minimizing submodel masking error. %We formally establish when magnitude selection is equivalent to Fisher selection fail under non-IID heterogeneous
federated learning. We theoretically formulate submodel selection through a Fisher-weighted quadratic masking surrogate and show that the raw Fisher top-$k$ rule implemented by FedFIbOS solves this surrogate under a Fisher-dominant ranking condition. The resulting method retains the convergence structure of the underlying masked
federated optimization bound. Fisher scores are efficiently estimated from empirical diagonal Fisher information using squared gradients, enabling stable and adaptive parameter selection without additional optimization overhead. Experiments on CIFAR-10, CIFAR-100, and AGNews under pathological and Dirichlet non-IID settings show FedFIbOS achieves ${\approx}10\%$ higher accuracy than the state of the art, with improvements becoming more pronounced under stronger heterogeneity.
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Human-Computer Interaction
Cyber Exodus: Burnout Symptoms, Exit Intention, and Peer Response in Online Cybersecurity Communities
Nadia Mehjabin, Ji Hyun Kim, Laura Barnes et al. (+3 more)
Security practitioners burn out at high rates, and the resulting attrition is itself a security problem. This workforce is hard to study: security operations centers are closed to outside researchers, studies that reach practitioners recruit through employers, and those who have disengaged most may have the least reason to answer an employer's survey. The same practitioners discuss their working conditions openly in online communities. We adapt the Burnout Assessment Tool, a validated clinical instrument, into a text annotation scheme and apply it to 354,861 posts and 296,442 replies from five online communities of cybersecurity practitioners. Checked against two trained coders on 100 posts, the annotation reaches a macro F1 of 0.75 across the four symptoms and 0.98 for detecting any burnout signal. We find that the four symptoms point to different problems at work, not to the same problem at different levels of severity. Exhaustion appears in almost any complaint about staffing or workload. Mental distance, a loss of belief that the work is worthwhile, is the only symptom unrelated to operational problems, and among posts with a single symptom it is accompanied by a stated intention to leave roughly twice as often as any other. Peer responses show the opposite pattern. When a poster says they are considering leaving, the mix of replies shifts toward career advice, but this shift is smallest for mental distance. The symptom most strongly associated with leaving is thus the one peers adjust to least, and a single burnout score obscures both patterns.
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All authors Nadia Mehjabin, Ji Hyun Kim, Laura Barnes, Koustuv Saha, Henry Kautz, Subigya Nepal
Computer Vision
A Multi-Modal Generative Model for Tomato Disease Leaves Understanding
Khang Nguyen Quoc, Minh-Phuoc Tran, Gia-Han Truong et al. (+1 more)
Artificial intelligence for plant disease analysis has advanced from task-specific classifiers to multi-modal models capable of jointly interpreting visual and textual information. However, practical deployment in precision agriculture remains limited because most existing approaches treat disease understanding as isolated prediction tasks, failing to capture the complementary relationships among symptom recognition, severity assessment, and question-driven diagnostic
reasoning. In tomato pathology, accurate interpretation of diseased leaves requires more than label prediction; it demands integrating visual symptoms with semantic context to support a comprehensive and explainable understanding. Here, we present
SOLAR, a
multimodal generative model that understands tomato disease spanning six question-answering tasks.
SOLAR learns to align visual features with task-aware
language representations by Fusion Expert module based on mixture-of-expert, enabling it to generate contextually relevant answers across diverse diagnostic tasks. By formulating tomato disease analysis as a generative Visual Question Answering (VQA) task,
SOLAR provides a flexible framework that supports multi-task inference within a single model while improving performance and cross-task knowledge sharing. We evaluate
SOLAR on $41,677$ images, including $216,209$ Question-Answering (QA) pairs to understand tomato leaf disease under both closed and open-ended QA settings. Experimental results show that
SOLAR consistently outperforms state-of-the-art
vision-only,
vision-
language, and task-specific models across all tasks, demonstrating superior accuracy, robustness, and
multimodal reasoning. These findings highlight the potential of generative
multimodal modeling as an effective direction for understanding of plant disease. The code for this study is available at https://github.com/EnalisUs/
SOLAR.
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All authors Khang Nguyen Quoc, Minh-Phuoc Tran, Gia-Han Truong, Luyl-Da Quach
Robotics
VABench: Measuring Embodied Spatial Intelligence through Visual Demonstrations, Active Perception, and Metric Control
Zhongbo Zhang, Jiayi Jin, Yifan Wang et al. (+4 more)
Spatial intelligence requires more than describing object locations. Under incomplete observation, models must identify and acquire missing evidence, interpret it in a common spatial frame, and act on it. We introduce VA-Bench to evaluate the complete observe-reason-act-revise loop. General-purpose MLLMs learn procedural context from RGB-only demonstrations, actively select camera viewpoints, issue metric Cartesian commands, and revise them from execution feedback. Models receive no privileged object poses, oracle trajectories, or learned action heads. A fixed model-agnostic controller executes only model-specified targets. VA-Bench contains 14 base task families (11 single-arm and three dual-arm), seven held-out geometry/layout variants, and a long-horizon five-object composition track. We evaluate 12 primary model conditions in three independent runs over the same 20 physically verified seeds per base task, reporting terminal success, nine trajectory-level behavioral diagnostics, and subtask progress. First, the best-performing model scores 100.0% on target localization and 78.9% on spatial relations in the annotated run. Its three-run macro-average task success is only 53.93+/-3.17%. Second, active camera control significantly improves task success over passive multi-view observation. In one matched comparison, success rises from 27.86% to 57.50%. Third, held-out
geometric transfer can reduce task success by over 30 percentage points. No model completes a strict long-horizon episode, despite substantial partial progress. VA-Bench thus tests whether general-purpose MLLMs can turn visual demonstrations and actively acquired evidence into successful
embodied action.
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All authors Zhongbo Zhang, Jiayi Jin, Yifan Wang, Zaibin Zhang, Haiwen Diao, Lijun Wang, Huchuan Lu
Computation and Language
From Parameters to Behaviors: A Survey of Model Fusion for Large Language Models
Shuo Cai, Yanggan Gu, Zihao Wang et al. (+8 more)
Model fusion integrates the capabilities from source models into a single target model. As of June 2026, Hugging Face hosts more than 2M models. This growing pool provides a rich base for model reuse and capability integration. Yet existing surveys often cover only separate parts of this space, and they do not provide a unified definition or a systematic taxonomy. This survey defines model fusion and organizes prior work into three levels: parameter-level, representation-level, and behavior-level fusion. We also review related metrics,
benchmarks, and applications, summarize current challenges, and identify future directions. Our goal is to provide a clear map of this area and support future work on model fusion. A comprehensive list of papers about model fusion is available at https://github.com/Baicaihaochi/Awesome-Model-Fusion-Survey.
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All authors Shuo Cai, Yanggan Gu, Zihao Wang, Yuanyi Wang, Yibo Yan, Wenjun Wang, Yuhang Liu, Guanghao Zhu, Sirui Huang, Ming Li, Hongxia Yang
Computers and Society
Funding the runners-up beats a golden ticket
Haining Wang
A golden ticket allows one reviewer to advance a proposal that the rest of a panel would reject. We assess its score-based rationale by comparing minority-support ranking with other rules for allocating the same number of places. We do so on 10,625 rejected submissions to a computer-science conference between 2017 and 2024, using later citation percentiles as the outcome. Minority-support ranking measured the gap between the two highest scores after adjustment for the panel mean and reviewer count. At a budget of 10% of the year's accepted count, panel-mean ranking selected submissions with a mean citation percentile 7.75 points higher than minority-support ranking (95% bootstrap interval 4.35 to 11.09). The difference was larger in the other sample periods and citation windows examined. At that budget, minority-support ranking was equivalent to a lottery within a margin of plus or minus 5 percentile points. Two further analyses, adjusted highest-score ranking and a matched comparison, found no clear citation advantage from minority support once the panel mean was accounted for. Ranking on score variance also outperformed minority-support ranking in a paired comparison. Estimates cover the 69.8% of eligible rejections whose later citations could be verified. These comparisons identify panel-mean ranking as a
benchmark for evaluating whether golden tickets select work that would otherwise be overlooked.
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Artificial Intelligence
Continual Enterprise World Model Discovery in Dynamic Systems
Shambhavi Mishra, David Vazquez, Perouz Taslakian et al. (+3 more)
In an enterprise system, updating one field can set another, create a record, or start an approval. These effects are produced by business rules that are not built into the platform but written by each organization and revised over time. An
agent working in such a system cannot predict the result of its own actions without knowing these rules. We study continual enterprise world model discovery, where an
agent starts without knowledge of these business rules and discovers them by interacting with records and observing the outcomes. From those observations it builds a world model, which it revises as the rules change. To evaluate this, we introduce EnterpriseWorldShift, built on a live ServiceNow environment with nine tables, 25 hidden rules and 600 evaluation actions. It presents four versions of the same enterprise world, with the tables and records held fixed while a rule is modified, then added, then removed, so that discovery, revision, extension and retirement are each tested in turn. Our Continual Discovery
Agent (CDA) builds such a model and carries it from one world to the next. It predicts the effects of the hidden rules more accurately than looking them up for each question, the approach taken by prior work, by up to 8.98 IoU points, and it answers from its own model without querying the running system.
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All authors Shambhavi Mishra, David Vazquez, Perouz Taslakian, Marco Pedersoli, Jose Dolz, Issam H. Laradji
Social and Information Networks
Finding Common Ground: Graded Communal Knowledge in Bluesky Starter Packs
Sagar Kumar, Lawrence Swaminathan Xavier Prince, Julia Mendelsohn et al. (+2 more)
Communication is made possible by common ground—the unspoken knowledge that people share and presuppose of one another, whether that be online or offline. In his conception of common ground, Clark (1996) distinguishes between personal and communal common ground, and asserts that the latter is graded: the more community affiliations two people share, the more common ground they share as well. Social media research has invoked this mechanism to explain how users connect, but it has gone largely untested because community memberships are rarely visible and, where they are, they are coupled to user interactions in a way that leads to conflating effects. To circumvent these challenges, this study repurposes Bluesky starter packs (SPs) as user-curated community affiliation labels. Across 191,648 pairs of users, we show that shared lexical repertoire—our proxy for common ground—grows monotonically with the number of SPs that users share, with users sharing a single pack being roughly twice as similar as equally connected strangers. A semantic renormalization of SP co-membership shows furthermore that it is more so the number of topically distinct communities, rather than the raw count, in which common ground is graded. Finally, we show that community co-membership adds to common ground independently of proximity in the Bluesky follow network. These results lead to the conclusion that community membership is a measurable, separable, and semantically structured carrier of common ground. Reading it as such makes common ground observable before an exchange rather than inferred from it, and thus opens the door for large-scale observational approaches to a set of questions that have so far only been posed in the laboratory.
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All authors Sagar Kumar, Lawrence Swaminathan Xavier Prince, Julia Mendelsohn, Brooke Foucault Welles, Nicholas W. Landry
Information Theory
A generalization of the map $\chi$
Xiutao Feng, Qiang Wang, Jingyi Yu et al. (+1 more)
The mapping $ \chi_n:\mathbb{F}_2^n \to \mathbb{F}_2^n$ defined by $y=\chi_n(x)$ with $y_i = x_i + x_{i+1}x_{i+2} + x_{i+2}$, where the indices are computed modulo $n$, has been widely studied for its application in lightweight cryptography. In this paper, we generalize this mapping and completely characterize all these shift-invariant permutations of the form $y_i=x_{i+u}+x_{i+v}(x_{i+w}+a_i)$, where $0\le u, v, w<n$ and $a_i\in \mathbb{F}_2$, $1\le i\le n$. We also determine their compositional inverses explicitly.
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All authors Xiutao Feng, Qiang Wang, Jingyi Yu, Anpeng Zhang
Programming Languages · Cross-listed from math.OC
Where the LLM Ends and Reliable Decisions Begin
Priyadarshan Patil, Abhishek Basu, Vikas Reddy et al. (+1 more)
Systems that turn natural-
language descriptions of optimization problems into solver-ready code generally use a
language model at every stage, including the final translation from a mathematical formulation into executable model-building code. We propose the ANVIL compiler architecture, where we separate these concerns. A
language model is called only once, assisted by constraint guidance based on problem type, to produce a LaTeX formulation. A deterministic compiler then translates that LaTeX into code with no
language model involvement. We describe the deterministic compiler (a normalizer, a recursive-descent parser producing a typed intermediate representation, analysis passes that bind symbols to a
dataset schema, and a code emitter) and evaluate it on the 354 easy and hard problems of the NLP4LP
benchmark. The compiler produced code for 329 of 354 formulations (92.9%), taking the deterministic path in every one of those cases and never falling back to model-generated code. Median compile time was below the 10ms resolution of our timer. Overall, our formulations achieved an accuracy of 98.9% over easy problems and 91.1% for hard problems. The gap between these compilation and accuracy figures is a key point of analysis, and we analyze it: formulations that failed to compile, problems that returned as infeasible, problems raising errors at runtime, and problems returning a wrong objective. Almost all of these errors trace back to the formulation rather than to the translation. ANVIL performs exceptionally well on leading
benchmarks by using
language models purely where they are effective, rather than as a catch-all tool.
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All authors Priyadarshan Patil, Abhishek Basu, Vikas Reddy, Abhinav Gupta