Computation and Language
Trajectory-Derived Confidence for Reliable, Resource-Aware Clinical Text-to-SQL Agents
Mincheol Daniel Song, Joshua Ward, Jake Jung et al. (+1 more)
LLM
agents for clinical text-to-SQL applications reason autonomously over multiple steps but cannot assess whether their own
reasoning or outputs can be trusted. In high leverage applications such as healthcare, this presents a critical risk where system mistakes can be costly. These reliability failures are also resource failures: an incorrect
reasoning trajectory spends computation budget on outputs that must be discarded. We introduce Sentinel, a trajectory-derived, classifier-based confidence layer that analyzes an
agent's
reasoning, code and database outputs to decide at three points whether to stop: refusing unanswerable questions before the
agent runs, halting doomed trajectories mid-run, and withholding untrustworthy answers at delivery. Here, utilizing Chow's rule, we optimize decisions under the EHRSQL shared task's Reliability Score, which penalizes incorrect answers given a utility weighting, and find on the
benchmark EHRSQL that Sentinel raises this score from +0.08 to +0.24 when mistakes have a low utility weighting, well above the +0.03 earned by refusing every question, with delivered-answer accuracy rising from 54% to 69% as coverage falls from 84% to 55%. At higher stakes the
agents we test rarely answer reliably enough to deliver, and Sentinel detects this on its own, abstaining to that same +0.03 where the unmonitored
agent scores -3.02. The same stopping decisions cut computation: at low stakes, where the system still answers, Sentinel eliminates 13-28% of
agent steps at little or no reliability cost.
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All authors Mincheol Daniel Song, Joshua Ward, Jake Jung, Guang Cheng
Systems and Control
One-Cycle Fault Classification and Faulted-Line Identification on the PROTECT-90 Dataset: An Initial Application Benchmark
Emad Abukhousa, Abdulaziz Qwbaiban, Saman Zonouz et al. (+1 more)
Open electromagnetic-transient
datasets are beginning to make reproducible learning-based protection studies possible, but the practical use of these
datasets still requires application-level
benchmarks that define timing, sensing, and validation assumptions. This paper presents an initial application
benchmark on the recently released PROTECT-90
dataset for two protection-oriented tasks: fault-type classification and discrete faulted-line identification. A compact one-dimensional convolutional neural network (CNN) is evaluated using post-inception windows of 0.25, 0.5, 1, and 2 cycles under strict episode-wise splitting. A non-convolutional multilayer perceptron (MLP) is also trained as an architecture-control baseline. The results show that both tasks are nearly saturated under full observability, with one-cycle test accuracies of 99.84% for fault type and 100.00% for line identification. The main performance variation appears under reduced observability: current-only inputs preserve line identification accuracy at 100.00%, whereas voltage-only inputs reduce line identification accuracy to 53.09% with the CNN and 50.57% with the MLP. This indicates that the limiting factor is measurement information rather than neural architecture. Additional stratified checks show stable performance across
topology states and fault-resistance bins, while CPU inference contributes only 0.528 ms to the one-cycle total decision time of 20.53 ms.
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All authors Emad Abukhousa, Abdulaziz Qwbaiban, Saman Zonouz, A. P. Sakis Meliopoulos
Human-Computer Interaction
LINK: A Corpus-Grounded Design Pattern Library for Educational Technologies
Lorena Lyon, Hyoungwook Jin, Vincent Cavez et al. (+3 more)
Designing learning interfaces is often an ill-structured problem: effective solutions depend on learning goals, subject matter, learner characteristics, and interaction context that are difficult to generalize. Designers must integrate knowledge from learning sciences, interaction design, and subject domains when translating learning goals into interface decisions. Yet, prior efforts to connect learning theory and design have largely focused on learning activities or specific technologies, leaving interface-level design knowledge fragmented across systems. In this work, we take a bottom-up approach to surface this knowledge from existing educational interfaces. We present LINK (Linking INstructional Knowledge to interfaces), a library of 27 recurring interface patterns, organized under nine higher-level design commitments, which describe broader learning-science-grounded goals for interfaces. As part of developing LINK, we conducted a user study (N=14) with designers to evaluate LINK's generative power. Our findings suggest that LINK helps designers generate interface ideas and articulate theory-grounded rationales for design decisions.
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All authors Lorena Lyon, Hyoungwook Jin, Vincent Cavez, Roy Pea, Xu Wang, Hari Subramonyam
Computer Vision
Kepler4D: Controllable Future Video Generation via 4D Scene State Evolution
Feiran Wang, Bin Duan, Junyi Wu et al. (+2 more)
Video world models aim to preserve scene structure and predict how dynamic objects evolve beyond visual observations. We present Kepler4D, a framework for future
video generation through explicit 4D scene state
evolution. Given a
monocular video, Kepler4D constructs a shared 3D representation of background geometry, object motion histories, coarse spatial supports, and semantic context. Chain-of-Motion summarizes observed motion and uses a
vision-
language model to select structured speed and heading decisions and decide whether to bound object-center height from below. A deterministic rollout converts these decisions into future object trajectories for inspection and editing before synthesis. We render the evolving proxies into
geometric controls for a pretrained
video generator, separating coarse object motion from the synthesis of appearance and articulation. Experiments on real-world
videos demonstrate that Kepler4D enables controllable object motion and plausible future rollout while preserving scene consistency.
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All authors Feiran Wang, Bin Duan, Junyi Wu, Gaowen Liu, Yan Yan
Computer Vision
Watermarks and Fingerprints as Soft Bindings for Content Provenance: An Open-Licence Benchmark for Images, Audio and Video
Seyedmahdi Kazempourradi, Ramtin Mojtahedi and Behrang Mohseni
Content-provenance standards such as C2PA let a platform recover a stripped manifest through a soft binding: an invisible watermark read from the content, or a fingerprint looked up in a registry. We benchmarked both families under one protocol, restricted to openly available models whose licences we audited, on public media, with false-match rates calibrated on held-out negatives and source-level bootstrap intervals for performance estimates. For watermarking we evaluated 25 image, 7 audio and 7
video configurations from 12 methods on perceptual quality, robustness, false positives and cost; for fingerprinting, 35 methods on registries of up to 98,985 images, partial edits and
adversarial attacks. PixelSeal gave the best balance for image and
video watermarks and AudioSeal for audio, but the error-correcting detector of every TrustMark variant fired on 5.9 to 15.3% of unmarked images, so a verifier should test the expected payload. Among fingerprints, copy detectors trained on non-commercial data detected up to 75.5% of transformed images at a pair-level false-match rate of $10^{-7}$ and DINOv2, the best permissively licensed method, 65.0%; near-copies in a product catalogue dominated the false matches, and no detection improvement from
geometric verification was observed under a matched calibration false-binding constraint in the evaluated image pipelines. On the same attacked copies the two families failed differently: the union of watermark and fingerprint successes covered 69% of image copies; fingerprints covered more audio queries, whereas expected-key watermark verification covered more
video queries.
Embedding a watermark moved the ISCC code of 62.0% of images past its match threshold, and platform-dependent colour conversion changed watermark bits between x86 and ARM hosts.
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Machine Learning
You May Be Running the Wrong Inception Crop
Jason Chuan-Chih Chou
A decade after its inception, Inception crop has become the standard crop-based data augmentation method for training deep
vision models. Not only is its practice of uniformly sampling crop scale and aspect ratio widely adopted, but also its lower and upper bounds, with the scale lower bound being the sole exception that is sometimes tuned. It is therefore surprising that the standard implementation in the TensorFlow / JAX ecosystem samples crop scale with probability density function $f(A) \propto \frac{1}{\sqrt{A}}$ unlike the PyTorch counterpart, which follows the original description. Motivated by this discovery, we train 522 ViT-S/16 models on the ImageNet-1k
dataset with various training budgets and crop scale distributions. We reach $78.78\pm0.09$ top-1 val. accuracy with 90 epochs of training budget and find that 1. Higher training budget requires stronger augmentation; 2. Lower tail of the distribution of the crop scale determines the augmentation strength of Inception crop; 3. Models trained with higher training budget exhibit sparser saliency, regardless of the crop scale distribution or weight decay. Based on 2. we revisit the performance of Beta crop, whose softer cutoff allows it to optimize model performance across training budgets with less compromise. We replicate 1. and 3. with Scion optimizer in addition to AdamW, suggesting that the results may be general.
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Robotics
Terrain-Dependent Intra-Cycle Leg Timing for Effective Locomotion on Granular Slopes
Long Zhao, Xingjue Liao and Feifei Qian
Locomotion on granular slopes such as sand dunes remains a fundamental challenge for legged
robots due to reduced shear strength and
gravity-induced anisotropic yielding of granular media. Using a hexapedal
robot on a tiltable granular bed, we systematically measure locomotion speed together with slope-dependent normal and shear granular resistive forces. While normal penetration resistance remains nearly unchanged with inclination, shear resistance decreases substantially as slope angle increases. Guided by these measurements, we develop a simple
robot-terrain interaction model that predicts anchoring timing, step length, and resulting
robot speed, as functions of terrain strength and slope angle. The model reveals that slope-induced performance loss is primarily governed by delayed anchoring and increased backward slip rather than excessive sinkage. By extending the model to generalized terrain conditions, we construct failure phase diagrams that identify sinkage- and slippage-induced failure regimes, enabling quantitative risk estimation for locomotion on granular slopes. This physics-informed framework provides predictive insight into terrain-dependent failure mechanisms and offers guidance for safer and more robust
robot operation on deformable inclines.
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Machine Learning
Consideration Circuits: Depth Separation and Universality Beyond a Single Softmax
Junjie Xiao and Huiwen Jia
Most feature-based choice models, classical and deep, score items and apply a single softmax. We introduce consideration
circuits (CC), feature-based models of multi-stage choice defined by directed acyclic graphs of multinomial logit (MNL) units. Source units assign probabilities to menu items, and internal units combine predecessor distributions using MNL weights computed from their probability-weighted feature summaries. On a three-item compromise task with fixed non-collinear features, menu-independent random-utility models (RUM), including a single MNL unit, suffer an error bounded away from zero. For CC, in contrast, we establish a sharp depth–norm separation: increasing depth from $2$ to $3$ reduces the optimal maximum taste-vector norm for error $\epsilon$ from $\Theta(\log(1/\epsilon)/\epsilon)$ to $\Theta(\log(1/\epsilon))$. The depth-$2$ lower bound holds for arbitrary width and menu-independent routing biases, while a five-node depth-$3$
circuit with zero routing biases attains the logarithmic rate. More generally, we characterize two
geometric conditions that are necessary and sufficient for approximating arbitrary deterministic choice tables on finite menu families. Under these conditions, depth $3$ suffices, while depth $4$ achieves optimal logarithmic norm scaling whenever the family contains a non-singleton menu. In experiments, standalone tree
circuits with fewer than $600$ parameters attain the lowest mean test negative log-likelihood (NLL) among the evaluated models on four fixed-pool
benchmarks and the Expedia temporal split. As output heads, CC generalize the linear MNL readout and lower mean test NLL for every tested encoder on Expedia and Trivago.
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Machine Learning
Ideal Paths for Approximating Logistic Gradient Descent Trajectories at Large Initialization
Junjie Xiao and Huiwen Jia
Modern training on a new task often starts from a previously trained model rather than from scratch, raising the question of how this initialization affects the subsequent training trajectory. Classical implicit-bias results characterize the direction selected by prolonged training, but this direction alone does not provide information regarding the intermediate behavior. We address this question through a
geometric approximation of full-batch logistic gradient descent (GD) trajectories on strictly linearly separable data, with large initialization of scale $R$ motivated by prior training. From any limiting normalized initial position, we use minimum-norm projection rules to construct a unique continuous ideal path consisting of finitely many linear segments. The path has two stages: negative-margin correction followed by minimum-margin growth. We prove that, after an explicit two-stage time reparameterization, the fixed-step GD trajectory divided by $R$ converges uniformly to this path on every fixed parameter interval as $R\to\infty$. Further, our quantitative error bounds account for initialization perturbations and the transition between stages. This approximation provides asymptotic formulas for peak evaluation loss and cumulative training loss. In particular, peak evaluation loss can grow linearly in $R$ even when both endpoint losses tend to zero. The cumulative losses in the correction and margin-growth stages, normalized by $R^2$ and $R$, respectively, converge to explicit limits. Experiments on controlled geometries and fixed image features complement our theoretical results.
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Cryptography and Security
From Temporary Access to Persistent Surveillance: Why Matter Matters in Smart Homes
Nicolas Nino, Timothy Minning and Le Guan
Matter aims to unify smart home ecosystems through an open, interoperable, and secure standard, relying on cryptographic mechanisms for device authentication and data confidentiality. However, its openness also exposes protocol details, credential structures, and implementation characteristics to adversaries. We show that an attacker with temporary physical access can exploit design and implementation flaws to extract credentials and impersonate devices and controllers. These replicas integrate seamlessly into the fabric, enabling persistent surveillance and control even after the attacker departs. Notably, the attack is vendor-agnostic and requires no device-specific reverse engineering, as long as the device is not physically secured. We validate its practicality through a proof-of-concept on a simulated smart home with both commercial devices and development boards. Our findings uncover previously underestimated attack surfaces in Matter, particularly under physical access scenarios, and motivate four targeted mitigation strategies.
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Machine Learning
ExpertMuon-Compass: Alignment-Guided Step Sizes for Mixture-of-Experts Training
Omatharv Bharat Vaidya, Ashwin Vinod, Pedram Akbarian et al. (+3 more)
Mixture-of-experts (MoE)
language models send each
token to a few experts, so each expert is trained on a different part of the data, and this part changes during training. With a shared learning rate,
Muon applies updates of roughly the same size to expert
matrices of the same shape, even when an expert's update is poorly aligned with its current gradient. We here propose ExpertMuon-Compass (Compass), which multiplies the
Muon step of each expert by two factors. A family factor compares the cosine between the expert's orthogonalized update and its gradient with the same cosine for the other experts in its layer. A scalar radius aggregates the
alignment between corresponding rows of the update and gradient into one step-length multiplier. Compass keeps the update direction and the momentum buffer of
Muon. In pretraining on FineWeb-Edu, Compass with Nesterov momentum on all
matrices performs as well as or better than
Muon, NorMuon, and other optimizers, with weight decay matched to NorMuon in the longer runs. Adding its factors to NorMuon gives the same or a lower loss than NorMuon. Compass is the most effective when the data seen by each expert varies during training, for example, as when the
languages of a
multilingual corpus arrive in separate blocks. With Compass, the expert load stays balanced, and the router assigns
tokens to experts more decisively. We also prove a perturbation bound for the two factors.
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All authors Omatharv Bharat Vaidya, Ashwin Vinod, Pedram Akbarian, Aditya Sai Ellendula, Connor T. Jerzak, Nhat Ho
Computer Vision
From Sight to Foresight: Predictive Spatial Reasoning in Vision-Language Models
Feiran Wang, Xiaoqi Wang, Ziwei Li et al. (+3 more)
Predicting future spatial states supports collision avoidance and timely decision-making in dynamic environments. However, existing
vision-
language models (VLMs) and
benchmarks for spatial
reasoning primarily focus on observed scenes, leaving predictive spatial
reasoning beyond the observed interval underexplored. To this end, we introduce SpatialMind, a metric-scale VLM for spatial
reasoning and future prediction. Its metric depth adapter anchors spatial
reasoning to real-world scale, while its progressive state chain establishes current spatial states and observed dynamics as the foundation for future prediction. Given a
video prefix, SpatialMind predicts distances, motion directions, and spatial relations in both observed and unseen future frames. For training and evaluation, we build a scalable data engine that grounds entity descriptions in metric geometry to generate question-answer pairs and state supervision. Using this engine, we construct the SpatialMind-30K
dataset and the SpatialMind-2K
benchmark, both covering driving and everyday
egocentric scenes. The
benchmark spans eight tasks across three levels: current-state understanding, observed-dynamics understanding, and future prediction. Experiments show that SpatialMind substantially outperforms both general and spatially specialized models on our
benchmark while achieving competitive zero-shot performance on VSI-Bench, OSI-Bench, and VLM4D.
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All authors Feiran Wang, Xiaoqi Wang, Ziwei Li, Wenbin He, Yan Yan, Liu Ren
Machine Learning
Dual-Scale Relational Graph Transformers for Ecosystem-Aware Fraud Detection
Mohsen Nayebi Kerdabadi, Xinrou Li, Yao Xiao et al. (+2 more)
Account takeover (ATO) fraud is a growing threat to digital banking, requiring effective detection while minimizing friction for legitimate customers. Production systems predominantly rely on tabular models that score sessions in isolation, discarding the relational structure of the underlying interaction network. Although graph-based models exploit relationships among sessions and network entities, they primarily reason over local neighborhoods and therefore capture only part of the problem: fraud risk depends jointly on the local relational structure surrounding a session and the evolving global state of the fraud ecosystem. We present HERMES (HEterogeneous Relational Micro–macro graph
transformer Encoder for high-risk Sessions), a dual-scale architecture that jointly models these complementary scales of information. Micro-GT captures local heterogeneous graph structure through structured, relation-aware
attention over a temporally safe session neighborhood. Complementing this local representation, Macro-GT models ecosystem-level context using non-anticipative
climate tokens that summarize fraud dynamics, platform shifts, and infrastructure reuse, together with adaptive class prototypes that track representative fraud and benign session patterns over time. Evaluated on more than 130 million high-risk transaction sessions from a leading U.S. financial institution, HERMES consistently outperforms production and strong graph-based baselines, achieving a 44.44% relative reduction in customer friction and a 24.66% relative improvement in fraud recall over the production system. Ablation and temporal-stability analyses further demonstrate complementary gains from local relational modeling and global ecosystem context across changing fraud regimes.
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All authors Mohsen Nayebi Kerdabadi, Xinrou Li, Yao Xiao, Zijun Yao, Xin Sun
Machine Learning
Dynamic Harness Search: Building Multi-Agent Systems Per-Query via Prediction
Som Sagar, Shasha Li, Hejie Cui et al. (+2 more)
Agent harnesses specify the roles, instructions, tools, and communication structure used to solve a task, and the right harness depends on the query. Because the value of each design choice is observable only through execution, tailoring a harness to each query has required either executing alternatives at inference time or costly manual design. We introduce SHIFT, which moves execution out of the per-query search loop. A local LLM architect learns a policy over harness-building actions from search, and a value function that predicts, from measured executions, a utility balancing accuracy against execution cost. For each query, Monte Carlo tree search uses these predictions to construct a harness. Across 9,193 tasks in six
benchmarks, from math to document and general-assistant tasks, with a Gemini 3.5 Flash executor, SHIFT attains the highest mean accuracy, about 80%, outperforming 17 baselines that span prompting,
prompt optimization, and workflow search, and exceeding the strongest baseline by 7.2 percentage points. A cheaper mode of SHIFT also attains a higher mean accuracy than every baseline while using 32% fewer execution
tokens than the strongest baseline. We further show that choosing structure, instructions, and tools jointly beats choosing only instructions or only tools by up to 9.1 percentage points, and that learned value selection identifies more accurate harnesses with lower execution cost from candidate pools.
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All authors Som Sagar, Shasha Li, Hejie Cui, Ransalu Senanayake, Sercan Ö. Ar{\i}k
Artificial Intelligence · Cross-listed from cond-mat.mtrl-sci
Agentic Resource Allocation for Batch Multi-Objective Bayesian Optimization in Autonomous Materials Discovery
Robert Robinson, Shakti Prasad Padhy, Sushant Sinha et al. (+6 more)
The discovery and development of advanced materials is a challenging process constrained by the high time and monetary costs of synthesis, processing, and characterization. The underlying design spaces can be enormous, often with multiple competing objectives.
Bayesian optimization (BO) provides a principled approach for efficiently navigating such spaces, but most workflows rely on fixed exploration-exploitation policies that lack the capacity to adapt to shifting constraints in dynamic campaigns typical of self-driving laboratories. In this work, we develop a multi-objective BO framework for
alloy design under resource constraints, benchmarking strategies for adaptive policy tuning at each iteration. Our evaluation covers a septenary refractory high-
entropy alloy (RHEA) system focused on maximizing melting temperature and minimizing density, and an Fe-Co-Ni-based soft
magnetic alloy system targeting saturation magnetization, coercivity, and hardness. We compare an exploitation-focused strategy, a fixed mixed exploratory/exploitative policy, and two distinct LLM-based adaptive strategies with different approaches to batch allocation and campaign signal interpretation, evaluated across baseline and mid-campaign resource event conditions including budget reductions, timeline cuts, and combined disruptions. Our results show that mixed allocation strategies accumulate substantially more mutual information than the exploitation-focused baseline at a proportionally smaller cost to hypervolume and optimization speed, with adaptive strategies outperforming a fixed-mixed allocation policy by adjusting their allocation in response to both evolving campaign statistics and resource constraints. These findings suggest that adaptive resource allocation offers a favorable tradeoff for materials discovery campaigns in reducing predictive uncertainty on Pareto-optimal compositions.
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All authors Robert Robinson, Shakti Prasad Padhy, Sushant Sinha, Sk Md Ahnaf Akif Alvi, Juan Florez Coronel, Brent Vela, Trevor Hastings, Douglas Allaire, Raymundo Arróyave
Artificial Intelligence
Auditing Pairwise Equivalence Judgments: Self-Critique Effects and Diversity Measurement in Multi-Agent Hypothesis Generation
Ji Young Byun, Anthony Hu, Jesse Rogers et al. (+3 more)
Multi-
agent systems built on large
language models (LLMs) are increasingly applied to scientific discovery and hypothesis generation. Both the effect of refinement and the diversity of the delivered set are hard to interpret before experimental ground truth exists, and both are typically reported by deciding whether pairs of generated hypotheses describe the same underlying mechanism. We study two evaluation questions that rest on this pairwise equivalence judgment: (1) how much self-critique changes delivered hypotheses beyond run-to-run variability, and (2) how the equivalence rule used to group hypotheses affects measured diversity. Across four proprietary instances, we hold opening hypotheses fixed, rerun the downstream workflow with 0, 1, and 5 critique rounds, and score matched hypothesis pairs with an LLM-as-a-judge. Relative to matched same-depth reruns, moving from 0 to 1 round produces 34.5 percentage points (pp) of additional mechanism-level divergence, whereas 1 to 5 rounds adds 1.3 pp. We then compare three equivalence rules: term frequency–inverse document frequency (TF–IDF) similarity, dense
embeddings, and the same LLM-as-a-judge. We construct controlled hypothesis pairs that either preserve the causal explanation through wording or biological-terminology changes, or replace one component of the causal chain while holding the rest fixed. All three rules are invariant to meaning-preserving edits, but when the initiating event is replaced, the LLM-as-a-judge identifies 83% of valid pairs as different mechanisms, versus 0% for TF–IDF and 8% for
embeddings; varying only the rubric that defines same mechanism moves this figure from 38% to 96%. Together, these results show that pairwise equivalence judgments are a measurement choice: how mechanism equivalence is defined affects both the estimated effect of self-critique and the measured diversity of generated hypotheses.
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All authors Ji Young Byun, Anthony Hu, Jesse Rogers, Roujia Wang, Manasa Kesapragada, Falgun Shah
Computation and Language
PB-GRPO: Learning Socially Adaptive LLM Agents from Persona-Driven Simulation with Preference-Batched GRPO
Jingquan Wang, Jun Yin, Xu Han et al. (+3 more)
Building LLMs that behave well socially, not merely correctly, requires Building LLMs that behave well socially, not merely correctly, requires more than producing locally helpful responses. A socially competent
agent must infer users' unstated goals, respect their preferences, and adapt as the conversation unfolds. These behaviors are inherently multi-turn and social, making them hard to optimize: real interaction data is scarce, and user preferences are typically latent rather than directly observable. To address these challenges, we build on a persona-driven social simulation environment (consisting of a persona library, LLM-based user simulators, and a user-satisfaction scoring system ranging from [0, 1]), to introduce preference-batched GRPO (PB-GRPO), a post-training
algorithm that learns socially adaptive policies from conversation-level feedback. Compared to vanilla GRPO, PB-GRPO computes advantages using a normalization estimated across a bucket of users with similar preferences, stabilizing training across a diverse social population. Empirical evidence shows that PB-GRPO improves models' social behavior over strong reinforcement learning baselines in our simulated environment.
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All authors Jingquan Wang, Jun Yin, Xu Han, Yongsheng Mei, Jie Hao, Bin Guo
Machine Learning · Cross-listed from astro-ph.IM
Shared Geometry Is Not Shared Physics: A Layerwise Test of the Platonic Representation Hypothesis in Astronomy
Kshitij Duraphe, Aravind Kannappan, Dun Li Chan et al. (+4 more)
In this paper we investigate whether geometrically aligned astronomical representations are also scientifically interchangeable. We analyze every layer's representation from 36 pretrained models using cross-matched optical images, infrared images, and spectra. All 139 analyzed model-survey comparisons show significant local-neighborhood
alignment somewhere in the network after accounting for the search over depth. However,
alignment does not generally increase with network depth and can be stronger for the changes between consecutive computational blocks than for the block outputs themselves. We find that geometry-selected stages under-perform label-selected stages in every image-transfer comparison; moreover, every final HSC-COSMOS-Web model pair is aligned while every
redshift transfer has negative R^2. Models can therefore recover a similar cross-survey geometry among astronomical objects without recovering a survey-invariant linear encoding of the physical properties tested here.
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All authors Kshitij Duraphe, Aravind Kannappan, Dun Li Chan, Okiki Famutimi, Yaswant Sai Ejjagiri, Michael J. Smith, John F. Wu
Artificial Intelligence
InvestigationWorlds: An Agentic Environment for Legal Investigation
Albert Yu Sun, Andrew Benard, Sil Hamilton et al. (+8 more)
We introduce InvestigationWorlds, an agentic environment for legal investigation. We build on an underused artifact of U.S. civil litigation: the summary judgment motion. This motion relies upon a record composed of real evidence exhibits, and results in a court-adopted hypothesis that is treated as ground truth for the purposes of deciding the motion. Each environment is built from a real U.S. Federal Court case retrieved from Public Access to Court Electronic Records (PACER) and augmented by an attorney-validated generation pipeline that synthesizes role-tagged documents around the original record. The resulting corpus admits multiple coherent factual readings, only one of which matches the court-adopted hypothesis. Evaluating on 100 cases, we find
agents often commit to incorrect hypotheses despite retrieving relevant evidence, struggling to distinguish the court-adopted hypothesis from alternative hypotheses.
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All authors Albert Yu Sun, Andrew Benard, Sil Hamilton, Anna Teresita A. Marcelo, Yong Jae Kim, Carl-Leander Henneking, Rundong Hu, Yuhong Wang, David Mimno, Bishan Yang, Igor Labutov
Cryptography and Security
What Gradients Add to Text Leakage in Split Language Models, Counted per Token and per Document
Georgios Politis and Evangelos Pappas
Split learning lets a client train a
language model on a server without sending its text. The client runs the first layers itself and sends the server only their output, a vector of numbers for each
token. During training, the server sends gradients back. We show that an observer at the split can rebuild most of the client's text from this traffic, and we measure how much the gradients help. On GPT-2, an attacker who holds only the publicly released weights of the client's layers recovers 94.20% of
tokens from the activations alone and 97.38% when it also sees the gradients, 3.17 percentage points more 95% interval [2.72, 3.64]. Counted by document, the difference is much larger. The attacker rebuilds 13.71% of 32-
token documents exactly without the gradients and 37.77% with them, because a document only counts when every
token is right. How we count also changes how good a defence looks. Secret mixup, which blends each outgoing vector with a decoy, stops the attacker from rebuilding almost any document exactly, yet the attacker still recovers 83-91% of
tokens. In a second experiment on GPT-2 and Qwen3-0.6B, where the server trains only a run of consecutive layers, the layer at which the run starts changes both model quality and leakage, even when the run's length is fixed. We recommend reporting leakage both per
token and per document, and treating what a split model sends as being as sensitive as the text itself.
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Systems and Control · Cross-listed from stat.AP
Multi-Target Tracking of Cooperating Swarms by an Ensemble Gaussian Mixture Filter
Zain Jabbar and Andrey A. Popov
Per-target labeled random-finite-set filters, such as the state of the art $\delta$-generalized labeled multi-Bernoulli filter, cannot represent cooperating, swarm-like, motion between targets. In this work, we present a new filter that can. The new particle filter couples the joint particle filter with ensemble Gaussian mixture filter such that a measurement updates the density at every particle instead of merely reweighting. We prove that the new particle filter converges to the joint particle filter in the limit of particle number and thus to the true
Bayesian posterior, under certain assumptions. Experimental results on two coupled systems, the Vicsek model and coupled Brownian motion provide evidence on the efficacy of the filter.
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Computation and Language
Representational Control over Self-Report & Behavior Coherence in LLM Risk-Taking
Rafal Kocielnik, Peiyang Song, Pengrui Han et al. (+4 more)
Self-report is an appealing low-cost probe of an LLM's dispositions, but recent work finds only selective agreement between what models report and how they behave. Prior accounts establish these patterns by prompting black-box LLMs, leaving open whether the gap is a prompting artefact or a fact about how the underlying constructs are represented internally. We investigate risk-taking, a consequential dimension of agentic decision-making, using activation steering to measure self-report and behavior under the same internal intervention. We survey nine steering-vector extraction methods spanning task-specific directives, the model's own task behavior, and dispositional descriptions at two granularities, evaluated on two behavioral tasks and two psychometric instruments across four open-weight LLMs. We find that (1) a shared internal intervention does not ensure shared responsiveness: directions built from trait descriptions move self-report but leave behavior at chance, directions built from the model's own task choices do the reverse, and only task-specific directives reach both, weakly. (2) Diagnosis dissolves that exception: removing surface confounders leaves the directives only 32% of their behavioral effect. The two channels are otherwise steered by near-orthogonal directions, each channel reachable by several independent constructions. (3) An intervention composing one behavior-moving and one self-report-moving direction moves both together; flipping one sign sets them in opposition, with reported and enacted risk pointing in opposite directions on 69-89% of flipped compositions in all models. These findings move the self-report-behavior relationship from a black-box observation to a representational one that can be inspected and controlled, motivating representational checks alongside behavioral evaluation.
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All authors Rafal Kocielnik, Peiyang Song, Pengrui Han, Myrl G. Marmarelis, Ramit Debnath, Dean Mobbs, R. Michael Alvarez
Computers and Society
Agent Reliability Profiles in Financial Services
Mike Hsu, Medha Bankhwal, Béatrice Moissinac et al. (+3 more)
AI
agents can take actions. At times, those actions can go beyond what is intended.
Agent reliability can be defined as assurance that an
agent will stay within intended bounds and operate within limits. Today, there is no shared framework or
language for describing, validating, and benchmarking the reliability of agentic deployments in financial services. This makes it difficult for financial institutions, vendors, and regulators to assess and trust
agents at scale, thus limiting the pace of development and adoption. A standardized, shared representation of
agent reliability would fill the gap. This paper introduces the
Agent Reliability Profile, a per-
agent unit of assurance evidence for
agent deployments in financial services. Each Profile records a bounded, falsifiable claim, this agentic system reliably functions within its operating boundary. We define "operating boundary" as an
agent having; (1) a defined autonomy tier, (2) a defined operational design domain, (3) defined classes of action, and (4) a defined control envelope. Production assurance progresses through three levels while the Profile schema remains constant: a Profile Builder compiles a Level 1 Asserted Profile from institutional evidence, a Profile Validator tests the deployment in its own environment to produce a Level 2 Validated Profile, and operation of the same tests by a qualified independent assessor produces a Level 3 Verified Profile. Separately a Benchmarked Profile reports results comparable across institutions under reference conditions. We describe the architecture, the artifact, the assurance ladder, the comparability flag, associated tools, an evaluation methodology, applications for financial institutions and supervisors, limitations, and a staged implementation program.
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All authors Mike Hsu, Medha Bankhwal, Béatrice Moissinac, Kevin Werbach, Lukasz Szpruch, Bennett Hillenbrand
Computation and Language
Copying Before Suppression: What Drives a Below-Chance Dip During Language Model Training?
Tejas Dahiya and Cole Blondin
Mechanistic interpretability usually studies fully trained models, yet the computations that drive a behaviour can change while the model is still learning the task. On the Indirect Object Identification task, a model should continue with the name mentioned once rather than the name mentioned twice. Pythia models pass through an early training window in which they prefer the repeated name, so accuracy in a choice between the two names falls below one half while
language-model loss on a fixed text sample keeps decreasing across the same window. The window reflects a temporary imbalance between two computations. We identify one cause of the wrong preference by selecting a set of
attention heads that write the repeated name, on
prompts separate from those used for causal evaluation, keeping that selection fixed, and then replacing each head's final-
token output with its average output on a separate set of non-repeated-name
prompts. This improves the correct-minus-repeated logit difference in a separately trained 160M model and in the official 160M, 410M, and 1B models. At 160M, the head that lowers the repeated name in the mature model shows little of its mature behaviour at this point. It directs less than one percent of its
attention to the repeated mention, and its output makes almost no direct contribution to lowering that name's logit. Both properties grow over the interval in which behaviour recovers. Across the 160M, 410M, and 1B models, transplanting the corresponding head's mature parameters into the early checkpoint recovers 35 to 68 percent of the total improvement in the correct-minus-repeated logit difference seen by the end of training. Related early-to-late reversals appear at further Pythia scales, in two independently trained GPT-2 models, and in OLMo. A mature
circuit can therefore conceal a transient causal configuration that shaped behaviour earlier in training.
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Computation and Language
LongSocialBench: Do Long-Context LLMs Understand Online Discussion Threads?
Xinyi Liu, Rinat Khaziev, Dilek Hakkani-Tür et al. (+1 more)
Long-context LLMs can now ingest entire online discussion threads, but understanding their social discourse requires more than reading a long document: models must track parent-reply relations, turning points, scoped subtrees, cross-branch contrasts, and participant trajectories. To test this structure-aware social
reasoning, we introduce LongSocialBench, a
benchmark of 1,462 verified human-authored multiple-choice items drawn from 94 complete Hacker News, Stack Exchange, and Reddit r/ChangeMyView episodes, with a median length of approximately 73K
tokens. Each item pairs a complete serialized discussion and reply structure with a four-option question, requiring models to recover structured social evidence. Released items are verified for answerability, option uniqueness, and evidence grounding. Across 18 models and 29 evaluation settings, current long-context workflows remain far below human performance. The best individual result comes from Claude-Opus-4.7, which reaches 63.0% when prompted to eliminate incorrect options before answering, compared with 72.4% for independent human readers. Averaged across all 18 models, the full-context Baseline scores 43.9%. Supplying the gold evidence scope raises this to 55.0%, showing that substantial errors remain even after the relevant thread region is identified. LongSocialBench shows that the missing capability is not context access or prompting alone, but social understanding over structured reply trees.
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All authors Xinyi Liu, Rinat Khaziev, Dilek Hakkani-Tür, Tarek F. Abdelzaher
Machine Learning
Sharp Convergence and Sample Complexity of Policy Mirror Descent for Average-Reward MDPs
Enes Arda and Atilla Eryilmaz
Policy mirror descent (PMD) has a mature finite-time theory in discounted Markov decision processes (MDPs), but less is known in the average-reward setting, a more natural objective for many control applications. We give a finite-time, finite-sample analysis of PMD in ergodic average-reward MDPs built around a single master recursion that governs convergence for any critic, without external regularization. Its specializations yield linear rates for exact, inexact-tabular, and linear function approximation (LFA) updates, with a superlinear regime for exact PMD. We complement these convergence results with end-to-end sample complexities of order $t_{\mathrm{mix}}^3/\varepsilon^2$ in both tabular ($|S||A|$-dependent) and LFA ($d$-dependent) settings. Our LFA sample complexity sharpens the prior best $t_{\mathrm{mix}}^5$ mixing dependence to $t_{\mathrm{mix}}^3$, and matching information-theoretic lower bounds establish that the critic's $t_{\mathrm{mix}}^3/\varepsilon^2$ sample complexity is unimprovable in both settings.
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Artificial Intelligence
CUAWright: A Minimal Unified Interface for Digital Agents
Yadong Lu, Theodore Lee, Yifei Li et al. (+5 more)
The prevailing approach to computer-use
agents couples a model with a domain-specific harness: a browser or desktop environment equipped with human engineered tools that are fixed before task execution. As models' coding capabilities improve, the GUI native and static harness prevents them from direct programmatic operation on system state, as well as flexible construction of tools. To this end, we introduce CUAWright, a minimal terminal harness of roughly 3K lines of code that uses bash commands as its sole action interface, and a file system as its evolvable space for dynamically creating tools and managing the context. We conduct comprehensive experiments across a wide range of digital tasks, and demonstrate that by giving the
agent a minimal, programmable interface, it achieves substantially stronger results compared to their GUI or hybrid CLI interface across a wide range of tasks. On OSWorld 2.0, CUAWright delivers a 33.2% relative improvement in partial reward while reducing estimated cost by 37.5% compared with the published GPT-5.5 baseline. On Online-Mind2Web and the long horizon Odysseys
benchmark, CUAWright substantially outperforms GUI native harness by 4.7% and 44.0% in success rate, respectively. Furthermore, we found the gains extend to CAD applications that require accurate visual understanding and CLI interaction: on CADGenBench and BenchCAD, our unified harness yields 8.1%-41.6% relative improvements over other CLI based harnesses with GPT-5.5. Together, these results suggest digital environments are far more programmable than their GUI interfaces imply, and a minimal terminal-focused harness is the key for better performance and efficiency.
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All authors Yadong Lu, Theodore Lee, Yifei Li, Lawrence Keunho Jang, Tianci Xue, Yu Su, Huan Sun, Ahmed Hassan Awadallah
Artificial Intelligence
The Independence Prior of SAEs Fragments Visual Concepts
Tommaso Mencattini, Giorgos Nikolaou, Donato Crisostomi et al. (+4 more)
Sparse Autoencoders (SAEs) decompose model activations into sparse combinations of interpretable dictionary atoms. Although SAEs are grounded in the Linear Representation Hypothesis (LRH), their objective smuggles in an additional prior: concepts across patches are treated as independent, an assumption clearly violated by natural images and by the activations they induce. We therefore specialize LRH to
vision through the Markov-Field Linear Representation Hypothesis (MFLRH), which adds the missing spatial dependencies to the LRH assumptions. We thus propose Spatial-SAE as an amortized MAP
estimator under the MFLRH. Spatial-SAE consistently outperforms standard SAEs in concept recovery and interpretability. Across four variants, it achieves a 96% average win rate on synthetic concept recovery and improves interpretability on DINOv2 activations, at a reconstruction cost concentrated in high spatial frequencies.
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All authors Tommaso Mencattini, Giorgos Nikolaou, Donato Crisostomi, Thomas Fel, Francesco Montagna, Emanuele Rodolà, Francesco Locatello
Machine Learning
Mingtian Tan, Palash Goyal, Mihir Parmar et al. (+6 more)
Real-world time series are frequently driven by exogenous events and structural shifts, rendering conventional
forecasting based solely on historical numerical observations insufficient. While
language models can retrieve external news, standard
retrieval-augmented approaches struggle with high noise, missing signals, and an inability to reason causally about event impacts. We propose SEER (Self-Evolving Event
Reasoning and
Retrieval), a closed-loop framework that dynamically optimizes event conditioning for time series
forecasting. SEER translates prediction errors into two decoupled feedback mechanisms: (i) a reflective
retrieval memory that refines subsequent search queries and filters spurious noise, and (ii) a persistent causal knowledge base that distills transferable domain dynamics. SEER enforces strict chronological boundaries across both event
retrieval and reflection, preventing look-ahead bias and data leakage. Across six volatile time-series
benchmarks, SEER consistently outperforms state-of-the-art time series foundation models and
language model baselines.
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All authors Mingtian Tan, Palash Goyal, Mihir Parmar, Sarkar Snigdha Sarathi Das, Chun-Liang Li, Nanyun Peng, Thomas Hartvigsen, Jinsung Yoon, Tomas Pfister
Machine Learning
Physics is the Best Teacher: Consistency Learning for Time-Invariant Operators of Chaotic Dynamics
Lufang Chiang, Jiachen Yao, Thomas Y.L. Lin et al. (+1 more)
Accelerating the prediction of long-term behavior in chaotic systems is crucial in scientific computing. However, existing methods rely on numerical solvers or autoregressive models that advance one small step at a time, which makes long horizons expensive. We instead view this problem as learning the system's time-invariant
evolution operator, which jumps the state across a large time span in a single evaluation. To this end, we derive the consistency equations a time-invariant operator must satisfy, with differential and compositional objectives in physical time. These equations also connect the learned operator to the physics-prescribed instant dynamics, enabling physics
embedding in consistency learning. Across five chaotic systems, we find that physics-distilled consistency makes both short-term trajectories and long-term statistics more accurate. The learned operator survives temporal extrapolation and requires one-tenth as many evaluations as autoregressive rollout, offering an efficient route to long-term simulation of chaotic dynamics.
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All authors Lufang Chiang, Jiachen Yao, Thomas Y.L. Lin, Anima Anandkumar