Artificial Intelligence
Quant Convergence: Bridging Classical Value Investing and Modern Factor Models for Systematic Equity Selection
Augusto Eiji Yamazaki and Hugo Garrido-Lestache Belinchon
Modern finance relies heavily on complex machine learning models to find patterns in the stock
market. However, as these AI models get more complicated, they often memorize short-term
market noise instead of finding companies with real, lasting value. We designed this research to test if Benjamin Graham's classic value investing rules could act as a mathematical "low-pass filter" to keep these modern models in check. We built three different sets of features - pure Graham rules, modern
market factors, and a mix of both - and tested them against highly complex models (XGBoost and AutoGluon) using 20 years of S&P 500 data. By applying a strict buy-and-hold strategy over a four-year test period (March 2022 to March 2026), the results showed that more complex
algorithms do not always win. While the AutoGluon model captured high returns (222.68%), it suffered a substantial 39.78% drop because it bought volatile tech stocks right before the
market crashed. On the other hand, the pure Graham Random Forest achieved the highest overall return (232.13%) with much less risk (1.38 Calmar Ratio). Furthermore, the Combined Random Forest successfully mixed momentum with Graham's rules, making a 202.91% return while keeping the lowest maximum drop (34.53%) of any model tested. Ultimately, this research proves that Graham's "margin of safety" isn't outdated; it is actually a highly effective way to prevent modern AI from taking on too much risk.
Read the full abstract ▾
Artificial Intelligence
Governed Shared Memory for Multi-Agent LLM Systems
Yanki Margalit, Nurit Cohen-Inger, Erni Avram et al. (+2 more)
Multi-
agent LLM environments require robust mechanisms for shared knowledge management. This paper formalizes the fleet-
memory problem and identifies four foundational failure modes: unauthorized leakage, stale propagation, contradiction persistence, and provenance collapse. To address these, we define explicit systems-level primitives: scoped
retrieval, temporal supersession, provenance tracking, and policy-governed
memory propagation. These primitives are implemented in MemClaw, a production multi-tenant
memory service, and evaluated via ArgusFleet, a reproducible harness testing four governance dimensions. Rather than a baseline comparison, this study measures a live production service, emphasizing real-world architectural insights and negative results. Key Evaluation Results Provenance: Successfully reconstructed 100% of depth-four derivation chains with correct writer identity at sub-second per-hop latency. Propagation: Demonstrated high intra-fleet visibility with zero cross-fleet leakage. Under strong write mode, write-to-visible latency was optimized to a single search round-trip. Production Architectural Issues Discovered Asymmetric Scope Enforcement: Tenant isolation held, but sub-tenant scope was initially bypassed on direct GET-by-id requests for
agent-scoped credentials (disclosed and remediated during the study). Pipeline Ordering Conflict: While contradiction supersession works for admitted writes, a synchronous near-duplicate gate can prematurely reject contradictory writes before the asynchronous contradiction detector can evaluate them. Conclusion: Long-context
retrieval alone is insufficient for production multi-
agent memory. Governed shared
memory demands explicit systems-level abstractions, and live evaluation is vital to expose enforcement and pipeline-ordering failures missed by design-only treatments.
Read the full abstract ▾
All authors Yanki Margalit, Nurit Cohen-Inger, Erni Avram, Ran Taig, Oded Margalit
Artificial Intelligence · Cross-listed from cs.CL
Poster: Exploring the Limits of Audio-Based Detection of Turkish Phone Call Scams
Arda Eren, Micheal Cheung, Youqian Zhang et al. (+2 more)
Scam phone calls exploit vulnerable communities worldwide, yet research on detection has focused almost exclusively on English and other high-resource
languages. In low-resource settings such as Turkish, detection is especially difficult, as annotated data is scarce and technological defenses remain limited. This research investigates how large
language models (LLMs) can support scam detection in Turkish by introducing the first public multi-modal
dataset of 100 aligned audio-transcript pairs of scam and benign conversations. We evaluate seven LLMs spanning three model families: Gemini 2.5 (Flash, Flash-Lite, Pro), GPT-4o, and Qwen (Max, Plus, Turbo), under three input conditions: raw audio, automatic
speech-to-text transcripts, and transcripts refined by a native speaker. Our results suggest that transcript-based inputs consistently outperform direct audio processing, while human-corrected and uncorrected transcripts perform comparably. By centering a low-resource
language and real world threat, this work highlights the urgent need for culturally and linguistically inclusive AI safety research and more robust multi-modal systems for fraud prevention.
Read the full abstract ▾
All authors Arda Eren, Micheal Cheung, Youqian Zhang, Grace Ngai, Eugene Yujun Fu
Artificial Intelligence
Reinforcement Learning for Computer-Use Agents with Autonomous Evaluation
Marta Sumyk and Oleksandr Kosovan
Computer-Use
Agents (CUAs) execute high-level user goals by perceiving and acting directly within graphical user interfaces. However, reinforcement learning for CUAs remains difficult because open-ended desktop environments rarely provide scalable, machine-readable reward signals: task success is often visually grounded and hard to specify with handcrafted reward functions or dense manual labels. We propose an RL fine-tuning framework that uses autonomous
vision-
language evaluation as a scalable supervision signal for GUI
agents. Given a final screenshot and the original instruction, a
Vision-
Language Model judges task completion and provides terminal feedback without task-specific heuristics or manual labels during policy optimization. Because autonomous evaluators are imperfect, we model their feedback as a noisy binary reward channel and derive a noise-corrected reward
estimator for Proximal Policy Optimization. Experiments across macOSWorld, Windows
Agent Arena, and OSWorld show that corrected evaluator rewards outperform both zero-shot baselines and raw evaluator rewards, improving success rates by an average of 12.6 percentage points over zero-shot performance and 5.1 points over raw evaluator fine-tuning. These results suggest that autonomous evaluation can serve as a practical reward signal for RL in GUI environments when evaluator noise is explicitly modeled and corrected.
Read the full abstract ▾
Artificial Intelligence
A specialized reasoning large language model for accelerating rare disease diagnosis: a randomized AI physician assistance trial
Haichao Chen, Songchi Zhou, Zhengyun Zhao et al. (+28 more)
Rare diseases affect millions of individuals worldwide, yet timely diagnosis remains a major public health challenge due to scarcity of specialized clinical expertise. While large
language models (LLMs) show promise to support rare disease diagnosis, current models are constrained by insufficient clinical deployability, limited clinically grounded evidence, and scarcity of training data. Here we present RaDaR (Rare Disease navigatoR), an open-source, compact
reasoning LLM (32B parameters) for rare disease diagnosis. RaDaR was trained with 49,170 publicly available free-text cases and 104,666 synthetic cases with
reasoning-enhanced training. RaDaR showed the strongest performance among evaluated open-source models, including the 671B DeepSeek-R1, across public
benchmarks and four external validation centers. In a retrospective cohort, RaDaR prioritized the final diagnosis before documented clinical suspicion in 61.06 percent of cases, corresponding to a potential lead time of 1.87 months and 50.18 percent of the within-center interval. In a randomized physician-assistance trial, RaDaR assistance improved physicians' rare-disease diagnostic accuracy by 21.44 percentage points compared with internet search alone. Synthetic-data ablations suggested that phenotype-anchored narratives provide useful training signal for long-tail rare diseases, with a monotonic scaling trend within the tested data range. Together, RaDaR and its development and validation framework provide a deployable rare-disease
reasoning model and a reproducible development framework for diagnostic AI under data scarcity.
Read the full abstract ▾
All authors Haichao Chen, Songchi Zhou, Zhengyun Zhao, Shikai Hu, Xianghong Jin, Hongwei Ji, Li He, Shuli Li, Yiming Qin, Xin Tan, Runfeng Shi, Yih Chung Tham, Jiaye Zhu, Ye Li, Ye Jin, Longhao Cao, Dawei Li, Honghan Wu, Hongqiu Gu, Guanqiao Li, Tudor Groza, Chunying Li, Dian Zeng, Weihong Yu, Gareth Baynam, Saumya Shekhar Jamuar, Min Shen, Shuyang Zhang, Bin Sheng, Sheng Yu, Tien Yin Wong
Artificial Intelligence · Cross-listed from cs.LG
A Fair Evaluation of Graph Foundation Models for Node Property Prediction
Oleg Platonov, Gleb Bazhenov, Dmitry Eremeev et al. (+1 more)
Due to the wide use of graph-structured data in different fields of industry and science, the development of Graph Foundation Models (GFMs) has recently attracted a lot of
attention. While many different types of models are called GFMs, particular interest has been paid to GFMs designed for node property prediction tasks, which is one of the most popular settings in Graph ML with lots of real-world applications from fraud detection in financial and social networks to recommendation systems for e-commerce and user-generated content platforms. While a number of GFMs for this task have been recently proposed, the field has not converged to a unified evaluation setting, and different works evaluate their models in widely different ways, preventing reliable comparison of GFMs with each other and with other types of models. In this work, we conduct a fair and rigorous reevaluation of 9 recent GFMs for node property prediction, comparing them to strong Graph Neural Network (GNN) baselines. We find that, among these GFMs, only the most recent ones based on the Prior-data Fitted Networks paradigm outperform well-tuned GNNs in predictive performance, although at a higher inference cost.
Read the full abstract ▾
All authors Oleg Platonov, Gleb Bazhenov, Dmitry Eremeev, Liudmila Prokhorenkova
Artificial Intelligence · Cross-listed from cs.DC
CrossPool: Efficient Multi-LLM Serving for Cold MoE Models through KV-Cache and Weight Disaggregation
Zhuoren Ye, Tianyu Wo, Dinghao Xue et al. (+4 more)
Emerging LLM services increasingly host many sparse MoE models, yet most models receive sparse requests and remain cold. This creates a GPU
memory problem: model weights are stable and model-determined, while KV-cache is transient and demand-determined. Because cold models rarely reach peak KV-cache demand at the same time, reserving worst-case KV capacity per model wastes
memory; a shared KV-cache pool can instead provision aggregate active demand. However, KV-cache sharing is not sufficient when weights and KV-cache remain in a monolithic GPU
memory pool. Static weights compete with dynamic KV-cache, and KV-head-limited
attention under cold, low-concurrency traffic exposes only a fraction of replicated KV capacity, leading to low GPU
memory utilization and weak long-context support. We present CrossPool, a serving engine for cold MoE models that separates FFN weights and KV-cache into two GPU
memory pools: a weights pool that consolidates FFN weights across cold models, and a KV-cache pool that dynamically serves active requests while keeping
attention local to KV-cache. CrossPool combines a KV-cache planner and virtualizer, a layer-wise pipeline scheduler that hides hidden-state transfers, and persistent kernels with control lowering to reduce CPU-GPU control overhead. With efficient GPU
memory pooling, CrossPool underpins bursty long-context requests and outperforms the state-of-the-art kvcached-based multi-LLM serving system, reducing P99 TBT by up to 10.4x.
Read the full abstract ▾
All authors Zhuoren Ye, Tianyu Wo, Dinghao Xue, Mingming Zhang, Yuchen Teng, Chunming Hu, Renyu Yang
Artificial Intelligence
On the Smallness of the Large Language Models Scaling Exponents
Sauro Succi, Peter V. Coveney and Alex Hansen
We discuss reasons why the scaling exponents of current Large
Language Models (LLMs) applications are indicating an unsustainable regime in terms of energy resources. We further show that attributing the smallness of such exponents to a numerical bias due to the neglect of a non-zero value of the loss function in the limit of infinite data (“pedestal effect") does not remove the unsustainability issue. Finally, the effects of the smoothness (roughness) of the data on the scaling exponents is commented upon based on an analogy with phenomenological models of fluid
turbulence.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.CR
Red-Teaming the Agentic Red-Team
Dario Pasquini, Michal Bazyli, Taras Fedynyshyn et al. (+1 more)
The use of agentic systems to perform offensive security operations has moved from a theoretical possibility to a commoditized capability. However, while the community has focused on creating more and more capable
agents, less
attention has been allocated to assessing the security of those systems. In this work, we present the first in-depth security analysis of the most widely used agentic systems for offensive security operations. We show that most of these tools share common design flaws that enable an active adversary to exfiltrate API keys, establish persistent footholds, and fully compromise the operator's machine, even when the
agent operates inside a sandboxed container. To support our analysis, we introduce a full cyber kill chain for such agentic systems, capturing the progression from initial LLM manipulation to lateral movement, persistence, guardrail bypass, and sandbox escape. Building on our security analysis, we derive a robust architecture for agentic offensive-security tools and propose actionable, broadly applicable design principles that mitigate the disclosed attack paths at the architectural level.
Read the full abstract ▾
All authors Dario Pasquini, Michal Bazyli, Taras Fedynyshyn, Artem Sorokin
Artificial Intelligence · Cross-listed from cs.CV
RetiSEM: Generalising Causal Models for Fragmented Biomedical Data
Inam Ullah, Imran Razzak and Shoaib Jameel
Learning causal models from fragmented biomedical data is challenging because clinical, molecular, and imaging variables are often incomplete or not jointly observed. We propose RetiSEM, a domain-constrained structural equation modelling (SEM) framework for causal graph recovery and mediation analysis under limited
multimodal resources. This proposed work organises variables into biologically informed blocks, applies forbidden-edge constraints, and decomposes pathway-level effects into TE, NDE, and NIE components. We evaluate RetiSEM across ten synthetic
benchmark scenarios that vary in dimensionality, nonlinearity, causal depth, and pathway structure, together with a fragmented real-world setting that combines NHANES clinical variables with externally derived retinal representations. This approach achieves lower structural error and higher causal accuracy than unconstrained baselines across the synthetic
benchmarks. In the real-data analysis, retinal variables behave mainly as downstream biomarker-like indicators, with smaller but detectable indirect effects. These findings support our strategy as an interpretable framework for testing structured causal hypotheses in limited-resource biomedical AI. The code and resources for this work are publicly available at: https://github.com/Inamullah-Colab/ReitSEM.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from eess.SP
Adaptive Machine Learning Framework for UAV Trajectory Optimization in O-RAN
Chenrui Sun, Swarna Bindu Chetty, Gianluca Fontanesi et al. (+3 more)
The deployment of unmanned aerial vehicles (
UAV) as open
radio units (O-RUs) in 6G cellular systems presents a promising opportunity to achieve scalable and adaptive network coverage. However, optimizing
UAV trajectories in dynamic and unfamiliar environments remains a critical challenge, particularly due to the need for extensive retraining in each new scenario. In this paper, we introduce a novel
UAV trajectory optimization framework that integrates enhanced continual transfer learning within the O-RAN architecture. The proposed system maintains a library of pre-trained models and employs a model selection mechanism to identify and transfer knowledge from the most relevant environments, minimizing adaptation time and improving efficiency. When no sufficiently similar model is available, a fallback model empowered by continuous refinements ensures baseline performance. The framework leverages real-world city maps and ray tracing techniques to enhance learning reliability and improve trajectory planning. Simulation results demonstrate that the proposed model selection-based transfer learning approach reduces convergence time by 44% to 56% compared to retraining from scratch, and up to 40% compared to traditional transfer learning without model selection.
Read the full abstract ▾
All authors Chenrui Sun, Swarna Bindu Chetty, Gianluca Fontanesi, Mahnaz Arvaneh, Walid Saad, Hamed Ahmadi
Artificial Intelligence · Cross-listed from cs.CV
video-SALMONN-R$^3$: Learning to ReWatch, ReAsk, and ReAnswer for Efficient Video Understanding
Yixuan Li, Guangzhi Sun, Yudong Yang et al. (+1 more)
Video large
language models (LLMs) are often constrained by computation and
memory budgets, leading them to use reduced frame rates and spatial resolutions, which may cause them to miss critical information for question answering (QA). A practical and efficient solution is a two-stage paradigm: first perform coarse
video understanding to localize relevant segments, and then re-watch these segments at higher temporal or spatial fidelity. In this paper, we present
video-SALMONN-R$^3$, the first end-to-end
video-LLM that enables re-watch through reinforcement learning without relying on chain-of-thought (CoT) cold-start. This design removes the need for costly CoT data annotations and avoids CoT-based supervised fine-tuning (SFT), which can otherwise degrade the pretrained
video understanding abilities. To address the mismatch between the
reasoning-first behavior induced by re-watch and the answer-first tendency of pretrained
video-LLMs, we propose a re-answer strategy, in which the model first produces a direct answer in the first watch and then refines it after re-watching. Finally, to improve question adherence during re-watching, we propose a re-ask mechanism that re-injects the query when revisiting localized segments. Experimental results show that
video-SALMONN-R$^3$ consistently outperforms both the base model and the QA-SFT baseline, while surpassing prior re-watch-based approaches with significantly lower computational cost. Code, models, and data will be publicly released upon acceptance.
Read the full abstract ▾
All authors Yixuan Li, Guangzhi Sun, Yudong Yang, Chao Zhang
Artificial Intelligence · Cross-listed from cs.RO
Yue Peng, Yongzhe Zhao, Artur Habuda et al. (+5 more)
Vision-
language-action (VLA) models have rapidly advanced generalist
robot manipulation by harnessing the semantic knowledge of pretrained
vision-
language backbones. However, their visual representations are typically grounded in 2D image coordinates rather than calibrated camera geometry. This limitation is especially pronounced in multi-camera systems, where views are coupled by known intrinsics and extrinsics but processed as independent images. We propose G$^3$VLA, a camera-aware
geometric module that injects calibrated structure into the visual-
token stream of a pretrained VLA without altering its action space or original action-learning objective. G$^3$VLA combines intrinsic-conditioned ray
embeddings, projective positional encoding (PRoPE), and bidirectional cross-view fusion to establish geometrically consistent representations across camera views.
Geometric supervision is provided by ground-truth point maps or confidence-gated $\pi^3$X teacher predictions, enabling training without depth sensors or manual
geometric annotations. On $\pi_0$, G$^3$VLA consistently improves performance on LIBERO and RoboCasa24 under both supervision regimes, while ground-truth supervision improves five-task RoboTwin2.0 averages with three cameras. Experiments on camera-shifted LIBERO-Goal and real
robots demonstrate improved robustness to unseen camera viewpoints. Controlled calibration studies across $\pi_0$ and $\pi_{0.5}$ show that these gains depend on correct camera geometry while remaining stable under mild calibration perturbations. These results demonstrate that grounding VLA visual
tokens in calibrated camera geometry provides a simple and general mechanism for improving
geometric consistency, multi-view
reasoning, and viewpoint robustness in pretrained
robot policies.
Read the full abstract ▾
All authors Yue Peng, Yongzhe Zhao, Artur Habuda, Khuyen Pham, Yanheng Zhu, Tran Nguyen Le, Fares Abu-Dakka, Li Guo
Artificial Intelligence
The Latent Bridge: A Continuous Slow-Fast Channel for Real-Time Game Agents
Bojie Li and Noah Shi
A real-time
agent for general computer use - with games as the most demanding case - must act within tens of milliseconds while still planning over seconds. These two regimes sit at opposite ends of the latency-quality tradeoff. A
reasoning VLM (Qwen3-VL-8B-Thinking) deliberates effectively but requires ~1.5 s per response - far too slow for a 15 Hz control loop. In contrast, a reactive VLM (MiniCPM-o 4.5) acts in milliseconds but underperforms on planning-heavy tasks. We couple two frozen models of matched scale (9B reactive, 8B
reasoning), leaving the communication channel as the sole trainable component. The standard coupling is a Text Bridge (T): the slow model writes a suffix the fast model reads. We introduce a learned continuous Latent Bridge (L) that projects the slow model's residuals into the fast model's input-
embedding space in a LLaVA-style manner, avoiding any text round-trip; both are compared against Fast-Only (F). On 7 Atari games and a driving domain (MetaDrive), tuning the action decoder per channel on held-out seeds, the Latent Bridge matches or beats the Text Bridge in every domain: it significantly improves two games (MsPacman +57%, RoadRunner +28%) and is a safe drop-in elsewhere. Combining both channels interferes destructively (RoadRunner -96%), so only one should be used. The benefit is highly predictable: the bridge helps if and only if slow
reasoning already beats fast reaction (T > F) - the Latent and Text gains over Fast-Only move together at r=0.93. MetaDrive is the controlled negative, where the Latent Bridge is demonstrably inert because the Text Bridge adds no value. We release replay recordings and reproducible pipelines.
Read the full abstract ▾
Artificial Intelligence
CompressKV: Semantic-Retrieval-Guided KV-Cache Compression for Resource-Efficient Long-Context LLM Inference
Xiaolin Lin, Jingcun Wang, Olga Kondrateva et al. (+3 more)
Long-context large
language model (LLM) inference is increasingly constrained by the
memory footprint and decoding cost of key-value (KV) caches, limiting sustainable deployment on resource-constrained hardware. Existing KV cache eviction methods typically apply heuristic
token scoring over all heads in GQA-based LLMs. These methods ignore the different functionalities of
attention heads, leading to the eviction of critical
tokens and thus degrading the performance of LLMs. To address this issue, we propose CompressKV, a resource-efficient KV-cache compression framework for GQA-based LLMs. Instead of aggregating
attention scores from all heads, CompressKV identifies Semantic
Retrieval Heads (SRHs) that capture both the initial and final
tokens of a
prompt and semantically important mid-context evidence, and uses them to select
tokens whose KV pairs should be retained. Furthermore, CompressKV allocates cache budgets across layers according to offline estimates of layer-wise eviction error. Experiments on LongBench and Needle-in-a-Haystack show that CompressKV consistently outperforms existing KV-cache eviction methods across
memory budgets. Notably, it preserves over 97% of full-cache performance using only 3% of the KV cache on LongBench question-answering tasks and achieves 90% accuracy with just 0.7% KV storage on Needle-in-a-Haystack. These results demonstrate an improved resource–performance trade-off for long-context LLM inference. Our code is publicly available at: https://github.com/TUDa-HWAI/CompressKV
Read the full abstract ▾
All authors Xiaolin Lin, Jingcun Wang, Olga Kondrateva, Yiyu Shi, Bing Li, Grace Li Zhang
Artificial Intelligence · Cross-listed from cs.CL
The African Language Tax: Quantifying the Cost, Latency, and Context Penalty of Tokenizing African Languages in Frontier LLMs
Olaoye Anthony Somide
Commercial large
language models bill, scale latency, and budget context per
token. Yet tokenizers assign more subword
tokens to the same meaning in some
languages than in others, so speakers of
languages with high
token-fertility pay a structural penalty before a model is ever invoked. This penalty is documented for
multilingual settings in general, but it has not been measured systematically for African
languages at the level of enterprise deployment economics and cognitive context capacity. We measure it across 20 African
languages spanning five
language families and three scripts (Latin, Ge'ez/Ethiopic, N'Ko; 19 appear in the primary FLORES-200+ corpus, with Nigerian Pidgin measured via MAFAND-MT only), using parallel corpora so that the
language effect is isolated from content. Across 11 frontier and open tokenizers on FLORES-200+, every African
language carries a tokenization premium above English (median 1.88x on GPT-5 / o200k_base, up to 8.92x for N'Ko); the penalty is largest for Ethiopic and N'Ko scripts (reaching 7-9x) and is near-invariant across corpora (FLORES vs SIB-200 Pearson r = 0.9998). Translated into deployment terms, this results in up to 8.9x inference cost and an equivalent generation-latency multiplier (N'Ko vs English on GPT-5; 7.4x for Amharic), and as little as 11% of English's effective context window. The best currently available tokenizer for African
languages, Gemma 4, reduces the mean premium from 3.31x (cl100k_base) to 2.38x, but no tokenizer eliminates the penalty. We release an open measurement tool (afri-fertility), a public leaderboard, a results
dataset, and mitigation guidance for African builders. The penalty falls hardest on the
languages whose speakers can least afford it, a digital divide encoded directly into the subword vocabulary.
Read the full abstract ▾
Artificial Intelligence
Theodore Papamarkou, Vladislav Smirnov, Viktor Mazanov et al. (+4 more)
Modern coding
agents pair LLM generators with various tools, including cheap diagnostics and expensive verifiers. The tool-use decisions are typically governed by orchestrators that often use fixed rules and ignore uncertainty. We formulate orchestration as cost-sensitive sequential hypothesis testing: a
Bayesian controller maintains a belief over candidate correctness and dynamically decides whether to gather more evidence, refine the candidate, verify it, or stop. Across six generators and nine coding
benchmarks,
Bayesian control proves to be most valuable when verification is costly and critics are informative but imperfect. Beyond control, the belief state yields an interpretable correctness score that outperforms
token-probability and raw tool-success baselines for uncertainty quantification.
Read the full abstract ▾
All authors Theodore Papamarkou, Vladislav Smirnov, Viktor Mazanov, Artem Vazhentsev, Preslav Nakov, Timothy Baldwin, Artem Shelmanov
Artificial Intelligence · Cross-listed from cs.RO
NoContactNoWorries: Estimating Contact through Vision and Proprioception for In-Hand Dexterous Manipulation
Soham Patil, Avirup Das, Sourabh Bhosale et al. (+1 more)
Perceiving physical contact is fundamental to dexterous manipulation. While
robots often rely on dedicated hardware tactile sensors, humans exhibit a remarkable ability to infer contact by integrating visual information with an innate sense of their body's pose and movement. Inspired by this
embodied perceptual skill, we investigate whether a
robot can learn to infer contact from
vision, an approach that also offers a scalable alternative to tactile hardware specifically for binary contact estimation, which faces practical challenges in cost, fragility, and integration. We present NoContactNoWorries, a
transformer-based
multimodal framework that fuses RGB-D
vision with the
robot's proprioception to infer binary contact states as a pseudo-tactile signal for hand-object interactions. We validate by training a single contact prediction model on multiple objects and show that the inferred contact signal supports downstream reinforcement learning
agents for in-hand object reorientation, generalizing to novel objects. Experiments in both simulation and on a real-world
robot validate our approach, highlighting the feasibility of inferring contact from
vision and proprioception. Project Page: https://soham2560.github.io/no-contact-no-worries/
Read the full abstract ▾
All authors Soham Patil, Avirup Das, Sourabh Bhosale, Spandan Roy
Artificial Intelligence
Heng Ping, Arijit Bhattacharjee, Peiyu Zhang et al. (+5 more)
Mixture-of-
Agents (MoA) architectures improve inference-time scaling by organizing multiple LLM
agents into layered
reasoning pipelines. However, existing MoA variants fail to sustain gains as depth increases, exhibiting degradation, early plateauing, or saturation. We propose ReM-MoA, a
memory-augmented MoA framework that sustains scaling through two mechanisms: (1) a Ranked
Reasoning Memory that persistently stores and ranks
reasoning traces from all layers using a comparative Reviewer
Agent, and (2) a Curated Diversified
Memory Routing scheme that exposes different
agents to distinct combinations of successful and failed traces, preserving exploration diversity while propagating high-quality
reasoning. We further introduce an optional multi-domain Reviewer
distillation pipeline that improves ranking quality through frontier-model supervision. Across five
reasoning benchmarks spanning math, formal logic, code, knowledge, and commonsense, ReM-MoA consistently outperforms prior MoA variants across both depth and width scaling, and its advantage widens with depth, establishing structured cross-layer
reasoning memory as a key missing mechanism for scalable multi-
agent inference.
Read the full abstract ▾
All authors Heng Ping, Arijit Bhattacharjee, Peiyu Zhang, Shixuan Li, Wei Yang, Ali Jannesari, Nesreen Ahmed, Paul Bogdan
Artificial Intelligence · Cross-listed from cs.CV
MedPCFM: Improving Medical Point Cloud Completion by Integrating Point Transformers and Flow Matching
Kamil Kwarciak and Marek Wodzinski
Medical point cloud completion is important for anatomical reconstruction and downstream clinical workflows, yet generative modeling in this setting remains insufficiently studied. We investigate completion through continuous-time generative modeling and introduce PCFM, a PTv3-backed flow matching approach for medical point cloud completion. We evaluate on SkullFix and SkullBreak, and additionally on the more recent Mandibular Defect
dataset. We build strong baselines by adapting PTv3 to a deterministic encoder-decoder completion model and by instantiating
diffusion completion (PCDiff) with both PVCNN and PTv3 denoisers. PCFM with PTv3 is competitive with the deterministic PTv3 baseline and achieves state-of-the-art generative performance across
datasets, while requiring substantially fewer sampling steps than
diffusion. At the best operating points, PTv3 also yields clear throughput gains, providing up to a 7$\times$ speed-up for PCFM compared to a PVCNN backbone. Finally, we study empirical scaling trends by varying model size and point cardinality, showing consistent gains with higher point resolution and informative trade-offs across model scales.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.CV
Transformation Behavior of Images in Latent Space
Christian Zöllner, Mozzam Motiwala, Aysel Ahadova et al. (+4 more)
Training of neural networks for histopathology classification tasks typically relies on data encoding into latent space, which reduces complexity and improves performance. There are several encoder networks available, either pretrained on general image
datasets such as ImageNET, or specifically on histopathological images. Training of encoder networks should be adapted to downstream tasks, allowing encoding of biologic/diagnostic content while rendering networks invariant to label-irrelevant transformations. This paper investigates the effect of classical image transformation on the latent space, using networks provided by Lunit Inc. and Bioptimus, both focusing on pathological images, and by Meta Research Team. We assess variance of
embeddings resulting from standard data transformations by comparing original and transformed image
embeddings and by contrasting them with random, unrelated
embeddings, using image tiles from hematoxylin/eosin-stained sections available in a colorectal tissue
dataset and the publicly accessible TCGA
dataset. Our findings show that
embeddings of original and transformed images are closer to each other than to random
embeddings, indicating robustness to transformations. However, they are not fully invariant, revealing that the encoder networks do not completely neutralize transformation effects in latent space, explaining why transformation-mediated augmentation of
datasets can improve performance. Significant differences were observed between general and histopathology-specific encoder networks.
Read the full abstract ▾
All authors Christian Zöllner, Mozzam Motiwala, Aysel Ahadova, Gerrit Anders, Robert Hüneburg, Jacob Nattermann, Matthias Kloor
Artificial Intelligence · Cross-listed from cs.SE
Detecting AI Coding Agents in Open Source: A Validated Multi-Method Census of 180 Million Repositories
Arsham Khosravani and Audris Mockus
Generative AI coding
agents are entering the open-source supply chain, yet their diverse and often invisible traces leave their prevalence poorly understood. We introduce a multi-layered detection framework that integrates configuration-file scanning, commit-message analysis, author-identity matching, and bot-signature lookup across World of Code (180M+ Git repositories), classifying
agent traces into four behavioral types. No single method captures more than a fraction of activity: multi-method detection identifies 850,157 Claude Code commits in one snapshot, of which bot-account lookup_the signal most adoption studies rely on_recovers only 28,154 (3.3%), a 30x relative-recall gap, so single-signal prevalence estimates are biased low by at least this factor. Every detection pattern is hand-validated (495 labels) with per-
cell precision and Wilson confidence intervals. Across snapshots from December 2024 to April 2026, commit-attributed
agents generate over 320,000 commits per month; Claude Code leads (886,122 commits across 17,295 projects) and dominates silent, configuration-file-only adoption (21,078 projects). Compared against an independent pull-request census (AIDev), the two channels capture nearly disjoint
agent populations_a PR census misses 79% of commit-detected Claude Code adopters and essentially all Codex adopters_and different kinds of work: PR-deployed cloud
agents (Codex, Cursor) surface as feature work, while commit-deployed in-editor
agents (Claude Code, OpenHands, Aider) surface as maintenance. The observed work profile follows deployment and detection mode rather than the tool itself, so no single channel is representative.
Read the full abstract ▾
Artificial Intelligence
Can Aggregate Invariants Accelerate Continuous Subgraph Matching? Limits, Laws, and a Dynamic Spectral Index
Minghao Chen and Jiale Zheng
Spectral filtering recently delivered substantial
pruning for static subgraph matching:
Laplacian interlacing rejects candidates whose neighborhoods cannot host the query. We study whether such aggregate structural tests can accelerate continuous subgraph matching (CSM) over dynamic graphs, and answer in three parts. First, lazily maintained spectral bounds are infeasible exactly where spectral
pruning has value: we characterize the tightest safe rule over a formalized perturbation relaxation and show that even it loses essentially all
pruning power within four touching updates. Second, exact maintenance is affordable when selective:
pruning utility and recomputation cost are anti-correlated across vertices – hubs provably never prune – so recomputing small-neighborhood spectra on touch sustains exact local spectra at microseconds per update, complete by construction. Third, integrated into a decoupled CSM
benchmark against an identical-minus-spectra control, the tests remove up to $51\%$ of candidates or safely skip up to $47\%$ of update enumerations, yet enumeration intermediates remain unchanged – beyond the gates' skipped first-level bindings, typically zero – across two engines, four real graphs, two stream types, and $77$ solved queries; a constructed radius-stratified workload confirms the instrument detects the exception when one exists ($-99.9\%$ intermediates, $748\times$ faster). Aggregate tests accelerate what scales with candidate sets – construction, list scans – never adjacency-guided exploration. We distill an intermediate-invariance methodology for evaluating CSM filters and release a reusable dynamic local-spectra index.
Read the full abstract ▾
Artificial Intelligence
Agentic AI for Bilevel Long-Term Optimization of Policy-Driven Physical Layer Systems
Bingnan Xiao, Chenhao Yang, Wei Ni et al. (+2 more)
Network operators' changing policies, service requirements, and stringent real-time constraints render existing methods designed with fixed objectives and constraints ineffective. This paper presents Agentic long-term performance optimization (Agentic-LTPO), a nested bilevel optimization framework that can be applied to adaptive physical layer problem configuration. The key idea is to employ agentic AI to generate upper-level configurations in a bilevel optimization structure, where evolving operator policies, environment summaries, and historical experiences are translated into structured lower-level optimization problem configurations. The lower level solves the problems with updated configurations for real-time physical-layer decisions. Considering
cell-free
MIMO beamforming as a use case, we embody Agentic-LTPO by designing a new multi-
agent decision process with
retrieval-augmented experience-based verification in the upper level, together with a closed-form beamformer in the lower level. Experiments demonstrate that Agentic-LTPO exhibits strong adaptability to dynamic operator policies and effectively enhances the system's long-term performance by 57.2% compared to traditional methods.
Read the full abstract ▾
All authors Bingnan Xiao, Chenhao Yang, Wei Ni, Xin Wang, Tony Q. S. Quek
Artificial Intelligence
Cycle-Consistent Neural Explanation of Formal Verification Certificates
Andoni Rodriguez, Alberto Pozanco and Daniel Borrajo
Formal verification produces machine-checkable certificates that attest to the satisfaction or violation of temporal properties, yet these certificates remain opaque to non-specialist stakeholders. We propose a cycle-consistent neural architecture that generates faithful natural
language explanations of verification certificates. A forward network NN1 maps certificates to explanations, and an inverse network NN2 reconstructs certificates from explanations; a symbolic verifier closes the loop, providing a differentiable faithfulness proxy. A pointer-generator mechanism ensures lexical grounding by copying state names directly from the certificate. We evaluate on 420 test certificates spanning six verification methods (bounded proof, k-induction, inductive invariant, lasso, reachability, witness pair) in both YES and NO verdict variants, drawn from a financial compliance domain with 207 named states. Our trained architecture, combined with a hybrid inference-time routing strategy, achieves 90.0% cycle-verified soundness, surpassing a multi- LLM few-shot baseline (76.1% for the best of 16 LLM combinations across four frontier models) by 13.9 percentage points. The neural model wins on 10 of 12 verdict/kind categories, with three categories reaching 100% soundness. The architecture offers 860x faster inference (185 ms vs. 160 s per certificate for the full multi-LLM baseline), offline operation, deterministic outputs, and zero per-inference cost. These results demonstrate that trained specialization outperforms general-purpose LLM prompting for structured certificate explanation, while eliminating the deployment constraints of cloud-based inference.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.DB
Entity Resolution via Batched Oracle Queries
Lorenzo Balzotti, Donatella Firmani, Luca Gagliardelli et al. (+1 more)
We consider an oracle that processes a limited batch of records at a time and clusters those that refer to the same real-world entity. We study how to interrogate such an oracle to resolve entities in a
dataset whose size is far larger than a single batch, and where no batch is guaranteed to contain all records of any given entity. We aim at a pay-as-you-go approach, to have full control over the costs (the number of oracle consults), while achieving the highest possible recall at every step. We formally cast this problem as batched entity resolution, prove that selecting optimal batches is NP-hard, and provide an optimal solution under a natural condition on entity sizes. Finally, we evaluate our approach on six
datasets and show its superiority over state-of-the-art baselines.
Read the full abstract ▾
All authors Lorenzo Balzotti, Donatella Firmani, Luca Gagliardelli, Giovanni Simonini
Artificial Intelligence · Cross-listed from cs.HC
Average Rankings Mask Per-Subject Optimality: A Friedman-Nemenyi Benchmark of EEG Motor-Imagery BCI Decoders
Xavier Vasques, Paul Barbaste and Olivier Oullier
Electroencephalography (
EEG) is the dominant non-invasive modality for
brain-computer interfaces (BCIs), yet reliable decoding of motor imagery is hampered by inter- and intra-individual variability. A recurring claim is that one decoding pipeline, most often a spatial or Riemannian method, is broadly preferable. We test the weakest version of that claim under the most favourable conditions. Using the Mother of All BCI
Benchmarks (MOABB) framework, we evaluated 1,056 decoding configurations (feature extractor x scaler x classifier), >340,000 subject-level model fits, across three public left-versus-right motor-imagery
datasets (PhysionetMI, 109 participants; Cho2017, 52; Zhou2016, 4) and two frequency bands (8-15 Hz, 8-30 Hz). Every model is fit and tested within a single session of a single participant, the easiest regime, giving every pipeline its best chance. We apply the statistics standard for multi-classifier comparison: Friedman omnibus tests, Nemenyi critical-difference analysis and Wilcoxon signed-rank tests with effect sizes. Covariance tangent-space projection (cov-tgsp) and Common Spatial Patterns (CSP) are the strongest families, but their ordering is
dataset-dependent and, on the largest and most heterogeneous cohort (PhysionetMI), statistically indistinguishable (Nemenyi p = 0.27; Kendall's W = 0.11). At the individual level the single best pipeline is optimal for only 35% of PhysionetMI participants, and nonlinear descriptors are best for roughly one third; matching pipeline to participant adds about seven accuracy points over the best fixed choice. The ranking is not an artefact of dimensionality, and classifier and scaler choices are secondary to the feature representation. Even in the easiest regime, no single pipeline dominates: a lower bound on the personalization problem and a quantitative case for participant-aware model selection rather than a universal decoder.
Read the full abstract ▾
Artificial Intelligence
ATRIA: Adaptive Traceable ECG Reporting with Iterative Agents
Donggyun Hong, Kyuhwan Lee, Junmyung Kwon et al. (+1 more)
Existing ECG report generation is tightly coupled – interpretation and reporting fused end-to-end, so errors propagate without stage-level recourse – while
agent-based systems decouple tasks but remain single-pass, never revisiting earlier outputs. Clinical ECG reporting instead unfolds iteratively, requiring progressive context integration and bidirectional editing. We present ATRIA, a multi-
agent ECG reporting system that mirrors the clinician's iterative workflow: it binds every report claim to its supporting evidence, flags statements unsupported by that evidence, incorporates additional context mid-session, and lets clinicians verify and revise individual findings rather than accept one opaque output. Because its
agents use ECG analysis models already in clinical use, the underlying findings are clinically trustworthy; and as a cloud-based web service, ATRIA is ready for immediate deployment. We demonstrate ATRIA through four interaction cases, with a live demo and
video available.
Read the full abstract ▾
All authors Donggyun Hong, Kyuhwan Lee, Junmyung Kwon, Yong-Yeon Jo
Artificial Intelligence
Age of LLM: A Strategic 1v1 Benchmark for Reasoning, Diplomacy and Reliability of Large Language Models under Fog of War
Arnaud Ricci
We introduce Age of LLM, a turn-based 1v1
benchmark in which two LLMs face off on a 13x7 grid to destroy the enemy base. Three stressors are deliberate: fog of war, full diplomacy (messages, ceasefires, ultimatums; uranium kept secret), and a reliability dimension where every turn must follow a strict JSON schema and an illegal action is silently discarded. The engine is private and each match uses a fresh random map seed and opponent, mitigating the data contamination that affects public
benchmarks. Models receive a (near) rule-only
prompt with no build-order advice (two tactical seed phrases were present during data collection; see Section 2.7). We
benchmark 15
reasoning models across 54 matches and 5,258 actions. Findings: (1) the
nuclear rush dominates (78% on the rules-coherent v0.11+ sub-corpus; 85% corpus-wide) with a sole-launcher signature that is largely mechanical under secret-simultaneous launch rules, not a cognitive deterrence failure; (2) military conquest is rare but faster (12.3 vs 18.9 turns); (3) diplomacy is prolific yet almost never consummated; (4) ~58% of illegal actions are fog/state errors, making the illegal-action rate a measure of belief-tracking; (5) – the least established, and the only one we label exploratory – a weak link associates reliability with winning. The corpus is small, unbalanced and not side-swapped, so the ranking is a preliminary descriptive view, not a contribution. Beyond ranking, the turn-by-turn traces of actions and messages make the corpus a lens on how LLMs reason under
adversarial uncertainty – their belief-tracking, spontaneous deception, and per-model cognitive "personas" – which we frame as a future research direction. We release the replay format, an isometric viewer and all replays; engine source on request.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from eess.IV
Female-RHINO: A Real-Time Scanner-Integrated Framework for Automated Quantitative Uterine MRI Analysis and Structured Reporting
Deepak Bhatia, Saad Ahmad, Smiti Tripathy et al. (+7 more)
Standardized assessment of uterine MRI remains challenging due to anatomical variability, observer dependence, and the lack of workflow-integrated automated analysis tools. This work presents Female-RHINO: (R)eproductive (H)ealth (I)maging A(N)alysis T(O)ol, a real-time AI-assisted framework for automated quantitative uterine MRI analysis and structured reporting during image acquisition. We present an end-to-end system that integrates inline communication with the MRI scanner and deep learning-based analysis to derive quantitative uterine biomarkers from sagittal T2-weighted pelvic MRI. The framework combines
segmentation and anatomical landmark detection models trained and evaluated on more than 500 multi-center
datasets spanning diverse protocols, vendors, and patient populations. It performs volumetry, detects and quantifies common incidental findings such as fibroids and Nabothian cysts, and extracts six anatomical landmarks for biometric assessment. Results are compiled into a structured clinician-oriented report with integrated visualizations, without manual interaction. Evaluation on independent retrospective and prospective cohorts demonstrated robust performance across varying acquisition settings. Mean Dice similarity coefficients were 0.82 for the uterus and 0.80 for fibroids, with lower but consistent agreement for Nabothian cysts. Landmark detection achieved a mean radial error of 3.7 mm. End-to-end processing was completed in under 70 seconds, enabling availability of results during the ongoing scan. Prospective deployment yielded immediate, standardized, and reproducible analyses supported by inter-observer agreement. The proposed system enables real-time scanner-integrated AI for automated uterine MRI analysis and reporting, with potential to improve standardization, efficiency, and clinical workflow in pelvic imaging.
Read the full abstract ▾
All authors Deepak Bhatia, Saad Ahmad, Smiti Tripathy, Maria Camila Bustos Vivas, Lieselotte Kratzsch, Anika Knupfer, Jordina Aviles Verdera, Susanne Schulz-Heise, Matthias May, Jana Hutter