Machine Learning
Pack and Extract: An Effective Graph-Based Approach for Feature Selection
Faisal N. Abu-Khzam, Joseph R. Barr, Cynthia Chaaya et al. (+2 more)
Many molecular biology data sets contain thousands of features, such as genes in single-
cell experiments or other omics data. Standard predictive models often perform poorly in such settings, as they tend to retain a large number of features and fail to adequately handle correlations among them. Combinatorial approaches, such as feature selection based on the Independent Dominating Set (IDS) model, have shown promising results, but appear to have been largely overlooked, possibly due to their high computational complexity. In this paper, we propose an improvement to IDS-based feature selection that (i) favors features exhibiting greater diversity and (ii) generalizes the notion of independent dominating set to a node packing model. The resulting formulation is used as an intermediate step within the feature selection process. We evaluate our approach on five genetic data sets and compare the results with those obtained using existing methods. Our experimental results demonstrate notable improvements in predictive accuracy over traditional models, as well as increased stability of the selected feature sets.
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All authors Faisal N. Abu-Khzam, Joseph R. Barr, Cynthia Chaaya, Nadim Obeid, Peter Shaw
Machine Learning
SECURE-AI: Multi-Signal Uncertainty Quantification for Trustworthy Acoustic Cough Detection
Sejal Solkhan, Feiya Xiang, Mayur Sanap et al. (+2 more)
Acoustic models for cough detection achieve high accuracy on curated
benchmarks but fail silently in deployment, where
speech, environmental noise, and audio unlike the training data induce confident misclassifications. In health monitoring, a confidently wrong prediction is worse than no answer at all. We present SECURE-AI (Self-aware Estimation of Cough Uncertainty for Reliable Evaluation of AI), a multi-signal uncertainty quantification framework that wraps a frozen MobileNetV3 cough classifier and estimates when its predictions should not be trusted, combining Monte Carlo Dropout, Temperature Scaling, and Mahalanobis distance in the
embedding space. On a held-out split of 12,161 Coswara recordings, the probability-space methods are individually insufficient: Monte Carlo Dropout assigns lower uncertainty to incorrect than to correct predictions, and temperature scaling improves calibration but not discrimination. The strongest signal is
embedding-space distance: Mahalanobis distance separates true positives from false positives (means 44.7 versus 63.4; Cohen's $d = 0.94$; AUROC 0.86) and, among predicted non-coughs, false negatives from true negatives (AUROC 0.82), so the same signal can flag both false alarms and missed coughs. At a descriptive operating point selected from labeled test data, retaining 90 percent of true-positive detections removes about two-thirds of false positives and raises precision from 59.9 to roughly 80 percent. A rejection threshold tuned on one split does not transfer to another (validation F1 of 82 percent falls to 34 percent on test), so we report threshold-independent AUROC as the main measure. An error taxonomy shows that failures are systematic, with most false positives related to
speech. The pipeline runs on Raspberry Pi hardware costing 30 to 90 US dollars. Probabilistic calibration alone is thus inadequate for reliable cough detection.
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All authors Sejal Solkhan, Feiya Xiang, Mayur Sanap, Geet Khatri, Edgar Lobaton
Machine Learning
Beyond Prediction: A Component-Level Study of Concept Recovery in In-Context Learning
Xufeng Liu, Fang Wan and Yi Liu
In-context learning (ICL) enables
language models to make accurate predictions from demonstrations, but prediction success alone does not reveal the extent to which the underlying concept has been recovered. Existing evaluations typically assess recovery at the level of the whole concept, making it difficult to identify which components are recovered and to separate model limitations from ambiguity in the demonstrations. We introduce a component-level framework for concept recovery that decomposes concepts into their defining components and uses controlled structural interventions to evaluate both component-level and whole-concept recovery under identifiable demonstrations. Across four instruction-tuned LLMs and three composition forms, we find that models often achieve high prediction accuracy despite poor whole-concept recovery, with systematic over-specification of relevant attributes and level constraints. Individual structural components are generally insufficient for whole-concept recovery, whereas jointly specifying complementary components, particularly relevant attributes and their level constraints, substantially improves recovery. However, this improvement is fragile: a single plausible but unnecessary level constraint can substantially disrupt recovery. Finally, joint supervision of recovery and implicit prediction substantially improves recovery while preserving prediction performance, with the learned capability transferring across composition forms. Overall, our component-level analysis provides a more diagnostic view of what models recover from demonstrations than the existing holistic view.
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Machine Learning
Factorization-Guided Training and Inference for Diffusion-Drafted Speculative Decoding
Jiakun Fan, Min Xiao, Qiang Wang et al. (+5 more)
Speculative decoding accelerates large
language model inference through parallel verification of
tokens proposed by a drafter.
Diffusion drafters further parallelize proposal generation, but greater proposal capacity or higher mean accepted
tokens (MAT) need not yield higher throughput. Because verification accepts only a consecutive prefix, early rejections can render later correct predictions ineffective, limiting MAT gains, while even higher MAT may not improve throughput if per-round latency increases. We introduce DScout, which uses two complementary factorizations to guide
diffusion drafter training and inference for higher throughput. For training, DScout factorizes position-wise loss into inclusion, weighting, and prediction loss. This guides the design and combination of mixed-horizon training, intra-block unmasking, and acceptance-weighted loss to align supervision with prefix acceptance and improve MAT. For inference, DScout factorizes throughput as the ratio of MAT to per-round latency, guiding target-conditioning depth, drafter depth, and inference block size selection to identify a high-throughput configuration for each workload. Across six
benchmarks, DScout achieves $3.84\times$ and $4.14\times$ mean speedups over autoregressive decoding on Qwen3-4B and Qwen3-8B, respectively, improving mean speedup over the state-of-the-art baseline by 9.1% and 7.1%. Across models and concurrency settings, our inference configuration policy provides up to 9.75% additional mean speedup over DScout's fixed configuration.
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All authors Jiakun Fan, Min Xiao, Qiang Wang, Neiwen Ling, Zhichao Liu, Zhihao Jia, Dimitrios S. Nikolopoulos, Zijun Wei
Machine Learning
Organizing LLM Inference for Aggregate Predictions from Behavioral Data
Varun Gupta and Vijay Kamble
When using a large
language model (LLM) to simulate a user population, how should we combine individual behavioral histories to predict the fraction of users who take a particular action? We study this question in e-commerce, predicting observed product-level conversion rates from browsing sessions without task-specific model training. We vary two design choices, each involving a bias-variance tradeoff. Summarizing a product's sessions before inference reduces cost but can discard predictive detail, adding bias, whereas predicting outcomes for individual sessions and averaging afterward preserves that detail but requires many calls to reduce variance. Evaluating products together in one
prompt can help the model infer what is typical in the setting, reducing bias, but also makes each prediction depend on which other products appear alongside it, adding noise. We evaluate both choices across five LLMs and two
datasets. Our main finding is that predictions from individual sessions outperform predictions from summaries when products are evaluated separately, but summaries lead when products are evaluated together. Comparative summaries achieve the highest Spearman correlation with observed rates in 12 of 15 settings, with no alternative significantly better. Comparison thus helps summaries far more than session predictions, overcoming the advantage of behavioral detail. We interpret this difference through a
Bayesian model of the second tradeoff, which suggests that comparison helps more when the inputs in a
prompt reveal more about differences across products, consistent with our results for four types of comparative context. Comparative summaries also incur a small fraction of the inference cost of the session methods. With the best model for each outcome, they attain 66-89% of the correlation of the best supervised
benchmark trained on thousands of labeled products.
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Computer Vision
VERDICT: Visual Extraction-Result Decoder with Integrated Confidence and Traceable Evidence
Harikrishnan P M, Goutham Vignesh, Siva Prasad et al. (+5 more)
Enterprises run schema-guided structured field extraction on documents with frontier
multimodal models, and accuracy keeps improving as new models and systems arrive. Two things have not kept pace: the evidence for each extracted value, meaning the location of the document it was read from, and how far to trust the value and that evidence. Neither is a component of most extraction systems today, so we build them as a framework that attaches to an existing extractor and leaves it untouched. We present VERDICT, a plug-in framework that uses a small
Vision–
Language Model (VLM) to ground/localize the evidence once per document over a fixed external extractor's output, then calibrates value and evidence confidence. Fine-tuning the VLM for evidence localization substantially improves visual evidence quality and, more importantly, expands VERDICT's selective-prediction operating range. At a strict confidence threshold $0.80$, VERDICT with the fine-tuned VLM gives usable precision–coverage trade-offs for both value and evidence confidence across the in-distribution and out of distribution
datasets, whereas the frozen setting often collapses to little or no coverage. Running an open-weight VLM for localization rather than a closed API makes its internal states available to the calibrator, and they are worth more than output log-probabilities: in-distribution AUROC rises from $0.730$ to $0.914$ for value confidence and from $0.681$ to $0.906$ for evidence confidence. This advantage does not survive a distribution shift. We show that the signal is displaced rather than destroyed, and that labeling roughly $120$ fields from the target
dataset takes evidence confidence from $0.641$ to $0.905$ on one
dataset and from $0.675$ to $0.829$ on the other.
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All authors Harikrishnan P M, Goutham Vignesh, Siva Prasad, Zishan Ahmad, Mahesh Basveshwar Deshmukhe, Varun V, Rohit Agrawal, Vishal Vaddina
Computer Vision
Toward AI-Driven Phytoplankton Event Detection from Image Classification: Temporal Aggregation Under Domain Shift
Ali Rohan, Hassan Shayan, Han-Gue Jo et al. (+1 more)
Imaging FlowCytobot (IFCB) systems generate large volumes of phytoplankton imagery that exceed the capacity of manual expert analysis. Although machine-learning approaches achieve high image-level classification performance, it remains unclear whether their predictions can provide ecologically meaningful temporal information for monitoring. This study investigates whether AI-derived phytoplankton classifications can reproduce temporal occurrence patterns and elevated-occurrence signals under realistic sampling and independent-domain transfer. A monitoring-oriented framework was developed using MedPlanktonSet and evaluated on an independent IFCB
dataset from the Swedish Meteorological and Hydrological Institute (SMHI). Eight biologically comparable taxa were selected as targets, while all remaining observations were retained as background to preserve the full image-stream denominator. Session-aware partitioning prevented temporal leakage, and alternative preprocessing strategies and convolutional neural network architectures were evaluated. Cross-domain experiments showed that preprocessing strongly affected generalisation. EfficientNet-B0 provided the strongest target-versus-background discrimination and was selected for temporal analysis. Despite residual image-level errors, AI-derived target-occurrence rates closely reproduced expert-derived signals after aggregation (mean absolute error = 0.0432, Pearson $r$ = 0.9696, Spearman $\rho$ = 0.9568). Agreement remained strong across multi-session windows, and elevated, strong, and persistent occurrence proxies were recovered in held-out testing. These results show that AI-based phytoplankton classification can support monitoring-relevant temporal indicators beyond image-level identification and support future operational early-warning development.
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All authors Ali Rohan, Hassan Shayan, Han-Gue Jo, Colin Moffat
Computer Vision
STLQ: Spatio-Topological Linkage Quantization for Approximate Nearest Neighbor Search
Kuizhi Liu, Junying Zhang, Xindi Ma et al. (+5 more)
Vector quantization is widely used for large-scale data compression and approximate nearest neighbor search (ANNS). Multi-codebook quantization (MCQ) is a practical paradigm that represents database vectors with compact codes and supports efficient
retrieval through asymmetric distance computation (ADC). However, existing shallow MCQs encode vectors independently within each inverted file (IVF) cluster, without exploiting local references. Therefore, we propose Spatio-Topological Linkage Quantization (STLQ), a topological shallow MCQ method that organizes each IVF cluster as a directed-forest with compact codes and scaling coefficients. Its basic encoding phase first quantizes vectors independently, while the linked encoding phase re-encodes relative residuals with respect to selected reconstructed parents, reusing local reconstruction information to obtain finer representations. The corresponding linked-ADC preserves linear scanning complexity. STLQ further compresses the scaling coefficients and directed-forest structure to control the additional
topology overhead. Extensive experiments show that STLQ achieves higher recall than the evaluated shallow MCQs at comparable bit budgets, although at a higher offline construction cost. Meanwhile, it has a competitive recall-QPS tradeoff against deep MCQs. The code is available at https://github.com/ighxiy/STLQ.
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All authors Kuizhi Liu, Junying Zhang, Xindi Ma, Xiangyu Wang, Qi Jiang, Sheng Gao, Chen Gong, Jianfeng Ma
Computer Vision
Trimodal Context-Aware Micro-Expression Recognition in Real-World Conversations: A Dataset and a Hierarchical Fusion Network
Junbo Wang, Yan Zhao, Shigang Wang et al. (+3 more)
Recognizing micro-expressions in real-world conversations is challenging, as these facial movements are brief and faint, and their interpretations are strongly dependent on the contextual circumstances of their occurrence. Most existing methods are de-contextualized, as they analyze isolated
video clips while disregarding the semantic and acoustic cues in the surrounding dialogue that help explain the emergence of an expression. Decades of psychological studies have shown that social context exerts influences on facial behavior over two key dimensions: how an expression is interpreted and how strongly it is generated to begin with. However, existing micro-expression research rarely incorporates such contextual factors into its models. As an example, provocative utterances from an interlocutor may elicit suppressed negative affective reactions, which isolated silent clips are unable to account for. We propose TriCoME, a trimodal
dataset consisting of visual, audio and text data sourced from real-world conversations. In this
dataset, each micro-expression is paired with its concurrent utterance and preceding context. We further annotate the context based on conversational role to denote two categories: external stimuli from an interlocutor's utterance and internal background from the subject's own utterance. On this basis, we propose the Hierarchical Focusing Fusion Network (HFFNet), which aligns acoustic and semantic streams at fine granularity, compresses long dialogue sequences, modulates
attention toward conversational roles via a context-type
embedding, and routes each sample between visual-only and
multimodal decisions based on visual confidence to prevent noisy context from degrading recognition performance.
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All authors Junbo Wang, Yan Zhao, Shigang Wang, Jian Wei, Qiming Zhang, Yu Wu
Computer Vision
Unbalanced Optimal Transport for Efficient Visual Document Retrieval
Hoyeon Shin, Jeongyeon Kim, Yeong Jun Koh et al. (+2 more)
Vision-
Language Models(VLMs)have advanced Visual Document
Retrieval (VDR) by enabling OCR-free, multi-vector representations. However, these models generate excessive
tokens to preserve dense visual details, which severely limits
retrieval scalability. Existing compression methods mitigate this overhead but rely on isolated heuristics that fail to balance
token importance, semantic redundancy, and coverage capacity. We propose a visual
token compression framework formulated as an Unbalanced Optimal Transport (UOT) problem. Our approach unifies these three objectives into a single alternating optimization process. Furthermore, we theoretically show that minimizing the transport cost upper-bounds the MaxSim
retrieval error, ensuring ranking stability. Extensive experiments on the ViDoRe V1 and V3
benchmarks demonstrate that our method preserves fine-grained document semantics and improves the efficiency-effectiveness trade-off over state-of-the-art base-lines. Code is available at https://github.com/shhhhhyy/Unbalanced-Optimal-Transport-for-EVDR.
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All authors Hoyeon Shin, Jeongyeon Kim, Yeong Jun Koh, Yeoneung Kim, Hanul Kim
Machine Learning
miniTE: Diagnosing and Bridging the Execution Gap in Native NVFP4 Reinforcement Learning
Zhen Li, Mingfa Feng, Yiming Zhang et al. (+6 more)
Native NVFP4 promises to accelerate reinforcement learning (RL) for large
language models (LLMs), but achieving stable learning across separate training and sampling engines remains challenging. We show that matching model weights and numerical formats is insufficient to ensure consistent policy evaluation, highlighting the importance of cross-engine numerical execution. We introduce miniTE, a lightweight framework that combines execution diagnostics, cross-engine numerical
alignment, and efficient native W4A4 (4-bit weights and activations) GEMM computation for LLM RL. It improves numerical execution consistency without modifying the RL objective, while reducing implementation overhead to realize the benefits of native low-bit computation. Long-horizon RL experiments on Qwen3-30B-A3B demonstrate stable training and
benchmark performance close to BF16, outperforming naive
Transformer Engine (TE) NVFP4 in evaluation quality. On single NVIDIA B300 GPU node, the optimized learner reaches 33,540
tokens/s, yielding 1.39x and 1.47x the throughput of BF16 and TE NVFP4, respectively, for complete learner updates. We released the framework, kernels, diagnostics, and training configurations to make native low-bit RL easier.
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All authors Zhen Li, Mingfa Feng, Yiming Zhang, Yang Yu, Changhai Zhou, Pengxiang Li, Dongkuan Wu, Congkai Xie, Hongxia Yang
Computation and Language
AbridgeKV: Compressing Long Reasoning into the KV Cache of Learned Pause Tokens
Jack Cai, Lijie Yang, Vasilis Kontonis et al. (+2 more)
Long chain-of-thought decoding is
memory-bound: the KV cache of the generated
tokens grows linearly with the test-time compute being spent. Evicting cache entries does not fix this, because a model never trained on a cache with entries removed generates longer, often up to the length limit. We instead train the model to compress its own output. AbridgeKV compresses each completed block of generated text into the KV entries of a few learned pause
tokens. These are extra input
tokens with trained
embeddings that produce no output. Once the pause
tokens have encoded the block, AbridgeKV frees the block's original cache; subsequent
tokens attend to the pause
tokens' KV entries in its place. Three novel choices make this work: the pause
tokens' KV entries take positions inside the block they replace, the cache budget is allocated across layers according to the measured value of each layer's entries, and compression operates on relatively large blocks. On 446 competition problems from AIME, HMMT, AMC, BRUMO, CMIMC and SMT with 32K-
token generations, keeping a quarter of the generated cache matches an identically trained full-KV model in accuracy and output length; the strongest published evictor at a matched budget writes far longer, and the closest trained compressor, retrained on the same data, loses 10 points. Served in vLLM, the cache is 3.5x smaller per sequence and a saturated H100 finishes a problem 3.2x faster than with full KV.
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All authors Jack Cai, Lijie Yang, Vasilis Kontonis, Dimitris Papailiopoulos, Shivam Garg
Computation and Language
Loose Lips: Understanding Privacy Leakage in Agentic Dialogue
Soumya Sharma, Ayana Hussain, Heber Hwang Arcolezi et al. (+4 more)
As LLM
agents are increasingly deployed in high-stakes enterprise settings, understanding how they handle sensitive information in agentic dialogue has become critical.
Privacy leakage in such settings requires
reasoning about whether information flows respect the contextual norms under which they originated. We extend CIMemories to CIMEMORIES DIALOGUE, a
benchmark of 4,410 two-
agent dialogue instances grounded in Contextual Integrity theory. Using this
benchmark, we systematically evaluate 7 open-weight models from the Qwen3 and Llama 3.1 families across conversation lengths of 1 and 6 turns under three prompting conditions. We find that task capability and
privacy leakage are fundamentally coupled: more capable models are systematically leakier, with no model in our evaluation able to decouple the two. Thinking models consistently leak more than their non-thinking counterparts, a pattern we attribute to the extended
reasoning surface created by chain-of-thought. Leakage compounds with conversation length, rising up to 3x from 1 to 6 turns. Our results suggest that the coupling between task capability and
privacy leakage is not an artifact of misconfiguration but a structural feature of current agentic systems.
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All authors Soumya Sharma, Ayana Hussain, Heber Hwang Arcolezi, Ulrich Aïvodji, Spandana Gella, Adriana Romero-Soriano, Golnoosh Farnadi
Artificial Intelligence · Cross-listed from quant-ph
Can the Quantum Fisher Effective Dimension Judge a Hybrid Quantum Neural Network?
Md Masum Mia, Amith Khandakar and Mohamed Arselene Ayari
Hybrid
quantum neural networks (QNNs) pair a classical feature extractor with a variational
quantum circuit, and the effective dimension of the
circuit's
quantum Fisher information
matrix (QFIM) is widely used to assess such models. This scalar is often interpreted as an indicator of trainability or accuracy, although these readings are rarely tested under controlled conditions. We therefore investigate what it actually measures. We define the effective dimension as the participation ratio of the QFIM diagonal, which lies between one and the parameter count, and we evaluate it across two image
datasets, four frozen backbones including a random-projection control, four
circuit depths on eight
qubits, and three seeds. During training, the effective dimension remains well below the parameter count, and its correlation with test accuracy ranges from -0.72 to +0.87, changing sign across seeds in half of the configurations. Increasing depth activates a larger share of the
circuit directions, from 0.38 to 0.91 of the parameter count, while the initialization gradient variance remains between 1e-5 and 1e-4, so no barren plateau (BP) appears at eight
qubits. At a fixed
circuit, the four backbones share an initial effective dimension of 6 to 9, even though CIFAR-10 accuracy ranges from 23.14% to 86.44%. These results show that the effective dimension characterizes
circuit geometry rather than model quality. We conclude with a practical protocol that assigns each diagnostic to the question it can answer.
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Robotics
From Hitter to Player: Separating Motor Execution and Strategy in Competitive Humanoid Table Tennis
Fanrong Dong, Ziyi Sun, Ziqi Han et al. (+6 more)
Competitive
humanoid table tennis requires two distinct abilities: executing a desired return and deciding where to return the ball. We separate these two problems, with the ball landing target serving as an interface between motor control and strategy. (1) We first train a whole-body motor policy conditioned on the desired landing target, which maps the target to an executable
humanoid stroke through a model-based strike planner and a learned controller. (2) We then improve the motor policy through self-play in closed-loop rallies, where the
robot learns to handle incoming balls generated by another player rather than only prescribed incoming balls. At this stage, the landing targets are still sampled without considering the opponent, so the policy improves its execution without learning game strategy. (3) Finally, we freeze the entire motor stack and train a stroke-level strategic policy to choose landing targets through competitive self-play. Motor self-play improves both landing accuracy and legal-return rate during rallies. More importantly, with the entire motor stack fixed, changing only the target-selection policy leads to large differences in game outcomes: the learned strategy wins 89%, 82%, and 73% of points against center, random, and away-from-opponent baselines, respectively. The learned strategy also changes where it sends the ball according to the opponent's position and the previous target, showing that self-play learns more than simply aiming at fixed "good" locations. On hardware, the same frozen execution stack and strategic interface support complete 11-point games against a recreational human opponent, including multi-stroke rallies with both forehand and backhand returns.
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All authors Fanrong Dong, Ziyi Sun, Ziqi Han, Junhan Sun, Lei Ye, Zetong Jing, Hao Zhao, Shaoxun Liu, Rongrong Wang
Robotics
Safety-Oriented Trajectory Ranking from BEV Perception Outputs
Leon Pohl, Martin Frauendorfer, Corinna Schmitt et al. (+1 more)
Bird's-Eye-View (BEV) representations are increasingly used as compact inputs for autonomous driving perception and planning. However, lightweight and interpretable methods for evaluating candidate ego trajectories directly on BEV outputs remain useful for vehicular safety workflows, rapid prototyping, and human-in-the-loop validation. This paper presents a modular trajectory ranking pipeline that consumes BEVFusion object detections and map
segmentations derived from nuScenes sensor data and ranks manually specified candidate trajectories according to safety-first criteria. The ranking prioritizes avoiding collisions and walkway violations, then smaller end distance to the target, and finally path efficiency. To study whether these rule-based rankings resemble human judgment, we created seven BEV traffic scenarios and compared the script output with preferences from a survey of 21 participants. Across 146 pairwise votes, 124 matched the trajectory ranked first by the evaluator, corresponding to an overall agreement of 84.9 %. The results indicate that simple BEV-centric safety heuristics can approximate human preferences in constrained trajectory choice tasks while remaining transparent enough for analysis,
dataset curation, and autonomous driving studies.
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All authors Leon Pohl, Martin Frauendorfer, Corinna Schmitt, Mirko Maehlisch
Robotics
Do My Samples Help? Compute Budget Allocation for Real-World Sampling-Based MPC in Contact-Rich Manipulation
Magnus Dierking, João Carvalho, Vince Kurtz et al. (+3 more)
Sampling-based model predictive control (SMPC) is increasingly practical for contact-rich manipulation because GPU-based physics simulators enable real-time evaluation of large numbers of non-differentiable contact rollouts. However, this computation budget is finite and can be spent in competing ways, including denser control exploration, representing dynamics or state uncertainty, replanning more frequently, or aligning the simulation better with the physical state. Yet, the resulting trade-offs are not well understood in real-world sim-to-real control. In this work, we address this question through a systematic study of how to allocate a fixed computation budget for SMPC in contact-rich manipulation. To enable controlled comparisons, we develop a GPU-based framework named Budget-Allocating Model Predictive Control (BAMPC), which runs on MuJoCo Warp and exposes dynamics uncertainty, state uncertainty, and temporal
alignment as independent dimensions of the computation budget while using the same control stack in simulation and on a real
robot. Across three contact-rich manipulation tasks, we find that the best budget allocation depends on the dominant sim-to-real error, with dynamics randomization not universally the most effective one.
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All authors Magnus Dierking, João Carvalho, Vince Kurtz, Jan Peters, Nils Dengler, Georgia Chalvatzaki
Machine Learning
Auditing LLM Program Search for Bermudan Stopping with Primal–Dual Certificates
Yuhua Qian
LLM program-search
agents are usually judged by the score that drives their search. We study LLM-driven search over Python exercise policies for Bermudan options, an optimal stopping problem where a trusted evaluator owns the simulation and a dual upper bound~$U$. For any policy, $0 \le V^\star - L_\pi \le U - L_\pi$; we report Monte Carlo estimates of both bounds with their sampling error. We audit 24 generation attempts from two pipelines, a six-pair feedback-representation ablation, and a holdout evaluated after selection was frozen. No selected generated program improves on Ridge LSMC consistently or by more than Monte Carlo uncertainty; holdout gap scores lie within $0.002$ of it. On six one-dimensional puts, a high-precision binomial reference places Ridge within Monte Carlo noise of optimal and attributes 99% of the estimated gap to dual-bound looseness, so this dual audits validity but cannot certify progress at the search's scale. Structured
memory showed no benefit over score-only
memory in this ablation. Whether search “worked” depends on reporting rules: retaining the initial reference, freezing selection before holdout, and counting failures.
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Data Structures and Algorithms
Grover Search for a Threshold-Fraction Variant of the Travelling Salesman Problem: A Circuit-Level Construction
Aayra Dhindsa Sawkmie
The Travelling Salesman Problem (TSP) is a classic combinatorial optimisation problem which is NP-hard in optimisation form and NP-complete as a decision problem. The standard question asks for the shortest tour that visits every city exactly once and returns to the origin. This paper studies a threshold-fraction variant, which we call the robust TSP, formulated as a promise problem: given a distance threshold $K$ and a promised fraction $p$, decide whether at least a fraction $p$ of all tours have total length at most $K$, or whether no such tour exists. We show that Grover's
quantum search
algorithm decides this problem using $O(1/\sqrt{p})$ oracle queries, independent of the number of cities $N$, with each query implemented by a
quantum circuit of
polynomial size in $N$. For any fixed robustness level $p$, the total runtime is therefore
polynomial in $N$. Measured in oracle queries, the
quantum algorithm offers a quadratic improvement in the dependence on $1/p$ over classical random sampling, which needs $\Theta(1/p)$ samples; because each
quantum query is costly, however, it is faster in total running time only when $p$ is very small (roughly below $N^{-4}$), where the number of queries is no longer constant. We describe the problem formulation, detail the oracle construction (edge-length loading, sequential
quantum addition, and threshold comparison), and analyse the query complexity to achieve the required result.
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Robotics
Terrain Traversal Without Motion Priors: Expert-Routed Humanoid Locomotion at Deployment Fidelity
Thanh Ly, Chien Le, Truong-Duy Dang et al. (+4 more)
State-of-the-art perceptive
humanoid locomotion is mostly built on retargeted motion capture, a generative motion prior, and a
distillation stage. We report a complete system that does not require this pipeline for a
humanoid to cross varied terrain. In its place, we provide a five-expert mixture routed by a terrain classifier over a sensed elevation grid, and target every foothold with a model-based planner, refined by six closed-form-gradient steps on an interpolated terrain-cost field instead of picked from a fixed grid. Driven through the same perception-to-actuation software a
robot would run, inside a second, deployment-fidelity simulator, it crosses an $85$\,m five-segment waypoint course $18$ of $20$ times end-to-end ($90\%$, Wilson $95\%$ CI $70$–$97$) and succeeds $20/20$ on four of five segments scored alone, where an ablated configuration without the mixture and without refinement reaches $0\%$ on both the stair and the hurdle segment. The continuous refinement moves $72.4\%$ of foot placements across at least one grid-
cell boundary, which produces real sub-grid correction. We also ran matched component studies to report what does not account for it: one expert equals five on the training distribution, four label-free routers collapse to near-uniform activation, and a self-supervised terrain
embedding loses to the privileged label.
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All authors Thanh Ly, Chien Le, Truong-Duy Dang, Duy M. H. Nguyen, Duy-Dung Le, Vien Anh Ngo, An T. Le
Machine Learning
CircuitTree: Composing Circuits into A Syllable Transcriber
Jiankun Wei, Ewan Dunbar and Gerald Penn
The goal of
circuit discovery is to decompose a monolithic neural network into tiny modular subnetworks which are interpretable in the sense that each does an identifiable subtask. We develop a practical method for composing arbitrarily many
circuits into a single model. We propose a greedy, density-scaled composition
algorithm that overcomes limitations of previous attempts to compose
circuits by naive union, then generalize it to $N$
circuits via CircuitTree, a new data structure built on a $\Theta(N\log N)$ tree-compose schedule. We compose
circuits pruned from HuBERT for classifying individual phonemes into a syllable classifier, a capability none of the 39 phoneme
circuits has individually. Chaining CircuitTrees into a hierarchy-of-
circuits pipeline, we transcribe open syllables at 64.8% accuracy, within 1.2 points of a 66.0% ceiling that shares no circuitry at all, with 99.5% of
circuit components shared.
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Machine Learning · Cross-listed from physics.chem-ph
Looking Under the Streetlight: Evaluation in Generative Molecular Dynamics
Simon Olsson, Frank Noé, Grant M. Rotskoff et al. (+1 more)
This position paper argues that the generative molecular dynamics (GenMD) community is not properly evaluating its progress: current evaluations let a model match what is reported while being arbitrarily wrong on what was claimed, which risks over-claiming advantage over classical baselines. This reflects a structural misalignment between common evaluation practices and the scientific tasks the field sets for itself. The reason is that the ground truth—exhaustive molecular dynamics on the systems of interest—is computationally costly, and this cost is what motivates the field. With ground truth inaccessible, evaluation drifts toward convenient proxies, and the resulting norms entrench easily. We identify misaligned evaluation patterns that persist as a community-level
equilibrium, and propose concrete observables, reporting standards, and
benchmark designs, both computational and experimental, that better reflect what GenMD aspires to deliver. Our aim is to give the field shared
language and tools for evaluation that match its scientific ambitions before today's conventions calcify into norms.
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All authors Simon Olsson, Frank Noé, Grant M. Rotskoff, Kresten Lindorff-Larsen
Computer Vision
TerraAgent: Demonstration-Grounded Multi-Agent Reasoning for Long-Context Remote Sensing Visual Question Answering
Jingwen Huang, Yiming Fang, Hanxiao Wu et al. (+2 more)
Remote sensing visual question answering (RS-VQA) requires answers that truly depend on image evidence, yet current remote sensing
multimodal large
language models (RS MLLMs) are still evaluated mainly by accuracy, which cannot reveal whether a correct answer was produced without using the image. This setting introduces three challenges: demonstrations retrieved across different ground sample distances (GSD), sensors, and city textures may be poorly matched; real queries often involve long visual contexts from multi-scale tiles, multi-temporal frames, and retrieved knowledge, where evidence can be diluted and errors can propagate through multi-step
reasoning; and prior-only answering remains invisible to standard metrics. We present AVA, a training-free framework that wraps a frozen RS MLLM with three modules. GDR performs GSD-bucketed, scene-graph-based demonstration
retrieval and filters demonstrations whose answers are unsupported by their images. MACE decomposes each query into an evidence-typed sub-question DAG, routes sub-questions to suitable perception tools, and records verified provenance in a shared evidence ledger with bounded re-perception. CVA audits the final answer through counterfactual perturbations of cited evidence, including ablation and swap, and computes a visual-dependency score for confidence calibration and evidence-forcing re-query. On RSVQA-LR/HR, VRSBench, and LHRS-Bench, AVA improves average accuracy by up to 2.40 points over the strongest same-setting baselines, raises the visual-dependency score from 0.28 to 0.49, reduces the prior-only answer rate from 46.2 percent to 21.7 percent, and lowers the
hallucination rate from 17.1 percent to 9.8 percent, with 2.6x the cost of a single backbone forward pass and no fine-tuning.
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All authors Jingwen Huang, Yiming Fang, Hanxiao Wu, Boyuan He, Keyi Luo
Computer Vision
TraceICL: Thread-Resolved Demonstration Curation with Consistency Calibration for Robust Multimodal In-Context Learning
Yigit Yilmaz, Elena Petrova, Mehmet Kaya et al. (+2 more)
Interleaved
multimodal in-context learning (ICL) lets frozen large
vision-
language models (LVLMs) acquire new tasks from a handful of image-text-answer demonstrations, yet it remains fragile: demonstrations bind to the wrong images, redundant exemplars crowd out informative ones, and noisy answers are copied verbatim into predictions. We present AVA, a training-free framework that organizes the demonstration pipeline of
multimodal ICL into three composable stages. A Thread-Resolved Decomposer (TRD) segments the interleaved context into per-sample threads and re-stitches them with explicit boundaries, restoring image-text binding. An Influence-scored Demonstration Selector (IDS) estimates each candidate's marginal influence with one extra forward pass, removes redundancy, and orders the survivors by descending value. A Weak-to-Strong Consistency Calibration (WSC) module scores each demonstration by a weak sibling model's reconstruction confidence and softly reweights its contribution during decoding. On five
vision-
language benchmarks with interleaved 4-shot
prompts, AVA improves the strongest same-setting baseline by +1.7 points on average, with gains of up to +2.4 points on VizWiz, shrinks the require-visual performance gap on ScienceQA-IMG, and retains most of its gain under 30% noisy demonstrations, at a 26% latency overhead that an offline cache amortizes. No gradients are ever computed; the LVLM stays frozen.
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All authors Yigit Yilmaz, Elena Petrova, Mehmet Kaya, Lucia Rossi, Amir Rahman
Software Engineering
CTML: A Comprehensive Framework for Continuous Testing in Machine Learning Operations
Bestoun S. Ahmed
The increasing use of machine learning (ML) systems in production has highlighted some important weaknesses in traditional software testing methods. Unlike regular software, ML systems can fail in specific ways due to their reliance on data, including issues like data drift, concept drift, and performance decline. To address these issues, this paper introduces the Continuous Testing for Machine Learning (CTML) framework. This framework is designed specifically for MLOps environments and offers a thorough, automated solution. It establishes a standardized way to evaluate ML systems across four main areas: data quality, drift detection, model quality, and pipeline validation. The CTML framework consists of a modular setup that includes a Test Registry, Test Runner, Result Store, and Integration Layer. This design ensures it integrates smoothly with existing Continuous Integration/Continuous Delivery (CI/CD) pipelines and MLOps platforms. We put this framework to the test in a predictive maintenance case study, which showed how effective it is at identifying issues like data drift, performance degradation, and bottlenecks in the pipeline problems that traditional testing methods might overlook. The findings indicate that CTML successfully uncovers critical failures in the system across all testing dimensions, providing useful insights to help keep ML systems running smoothly in production. Overall, this work presents a new, integrated framework that connects academic research with industry best practices, pushing forward the standards in MLOps quality assurance.
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Computer Vision
CORAL-KV: Compact Cross-Layer Prediction and Residual Coding for Autoregressive Video KV Caches
Albert Ge, Akash Haridas, Utkarsh Saxena et al. (+5 more)
Autoregressive
video diffusion models maintain a growing
attention key-value (KV) cache, making
memory a key bottleneck for long
video generation. Recently, clustering-based quantization has emerged as an effective method to compress
video KV caches by grouping similar key and value vectors then quantizing residual errors. However, when later frames attend to the compressed history, small reconstruction errors can compound as the
video grows. Beyond the within-layer similarity exploited by clustering, we find strong dependencies across cache layers in
video models. This motivates Coral-KV - Calibrated Offline, Residual-coded, and Adapted Locally - a KV-cache compression framework that uses cache states in each layer to predict keys and values in subsequent layers, then quantizes the remaining prediction residuals. We construct compact block-diagonal-plus-low-rank (block-DPLR) predictors: block-diagonal components model channel relationships within each
attention head, while a global low-rank term models dependencies between heads. Coral-KV exposes a flexible fidelity-
memory trade-off: it can reuse its predictors across
videos or refit lightweight block-diagonal predictors to the current
video for higher fidelity. Across extensive evaluations on both HY-WorldPlay and MAGI-1, our reusable configuration improves PSNR over QVG-4 by 0.32 dB and 4.95 dB, respectively, while using 48% and 37% less cache
memory. Against QVG-Pro, our
video-adapted configuration improves MAGI-1 PSNR by 3.77 dB while using 33.7% less cache
memory. Coral-KV also maintains higher reconstruction fidelity over extended generation and reduces temporal discontinuities while retaining comparable generation latency.
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All authors Albert Ge, Akash Haridas, Utkarsh Saxena, Ruijing Yang, Ian Colbert, Frederic Sala, Vikram Appia, Emad Barsoum