arXiv:2607.08891v1 Announce Type: new
Abstract: Engineering design intent is often communicated through rasterized orthographic drawings. However, downstream workflows inherently require editable and parametrically defined 3D computer-aided design (CAD) models. To bridge this gap, we introduce Ortho2CAD, a vision-language model (VLM) specifically designed to translate rasterized orthographic drawings directly into editable CadQuery code, which can then be seamlessly converted into 3D CAD models. To train the model effectively, we utilize supervised fine-tuning (SFT) for instances where explicit CadQuery code labels already exist, and we apply geometry-grounded reinforcement learning (RL) to optimize the model in scenarios where ground-truth labels are absent. To enable learning at scale, we create a pythonOCC-based drawing generator that renders first-angle orthographic projections from STEP models, complete with dashed hidden lines and key dimensions. On existing datasets encompassing settings both with and without CadQuery supervision, we generate orthographic drawings and show that our model produces 100% syntactically valid code. Moreover, it achieves a 3D CAD intersection-over-union (IoU) accuracy that surpasses all baselines, with an average relative improvement of over 7% compared directly against the next best performing model. We show that leveraging VLMs with SFT and RL techniques can effectively pave the way forward for orthographic drawing to 3D CAD reconstruction. Our implementation is available at https://github.com/AdityaJoglekar/Ortho2CAD.
Science Journals
arXiv:2607.09519v1 Announce Type: new
Abstract: We present DemoBridge, an toolkit that turns a single-view RGB stereo recording of a human hand demonstration into an executable, physics-validated robot-arm trajectory. Retargeting across the embodiment gap is hard. A robot arm reaches a target with a long, articulated body whose links carry far more collision volume than a hand. Solving inverse kinematics for the mapped end-effector pose often yields no collision-free solution, and a trajectory imposes this at every waypoint. A single view adds noise, leaving the demonstrated reference inaccurate. At the core of DemoBridge is a single collision-aware planner. It optimizes the whole joint trajectory at once, reasoning jointly over alternative grasp poses, whole-arm and grasped-object collision, and fidelity to the demonstrated path. A physics simulator runs in the loop. It validates each phase as it is produced and backtracks on failure, so a demonstration that cannot be reproduced as given is re-planned rather than discarded. The resulting action sequence is dynamically stable and faithful to the demonstrated manipulation. It also doubles as a ready-to-use simulation rollout for policy learning. Grasp timing is inferred automatically, and the perception backends, robot, and pipeline stages are swappable from configuration. We evaluate whole-pipeline retargeting on three real-demonstration tasks and the planner on a controlled synthetic benchmark. Our code is available at https://gitlab.kuleuven.be/u0123974/demo-bridge/ .
arXiv:2607.09323v1 Announce Type: new
Abstract: The shortage of healthcare staff is a challenge in geriatric care. To address this, robots can be integrated into care settings to provide assistance and emotional support. A promising application is walking guidance, particularly benefiting older adults as navigation skills deteriorate with aging. As walking guidance involves direct contact, the aim of this study is to understand how older adults perceive and respond to different touch modes during guided walking. 24 older adults (68 - 88 yrs.) walked four times a ten-meter trajectory guided by the robot TIAGo Pro in four contact conditions: no physical contact (NC); physical contact through holding the robot's wrist with the hand (HH); physical interaction through linking arms with the robot (LA); and physical contact through resting the forearm on the robots forearm (FC). A multimodal assessment approach included electrocardiogram, electrodermal activity, contact force, distance to robot, and questionnaires. Physiological results reveal a slight increase in stress levels during robot interaction. Behavioural and subjective measures, however, show overall acceptance of robotic touch. The two conditions corresponding to larger interaction forces (HH and FC) were associated with lower relative distances between participant and robot, indicating a higher trust and confidence. Questionnaire responses supported these findings, evidencing greater perceived safety, trust and comfort in these conditions. This study provides insights for the design of robotic walking guidance assistance, indicating that gentle, stable touch is preferred by older adults in comparison to contactless interaction.
arXiv:2606.18443v2 Announce Type: replace-cross
Abstract: In a standard two-level Ramsey interferometer, the accumulated signal phase is linearly mapped to the readout phase. Here, we introduce three-level Ramsey interferometry, in which projected interference between internal paths reshapes this mapping through a noncyclic geometric phase response. Near a geodesic-closure transition, a small accumulated signal phase produces a sharply amplified readout-phase shift. We quantify the accompanying gain--visibility tradeoff and identify a finite operating window in which the amplified response can yield a net signal-to-noise-ratio gain in the presence of additional technical phase noise. A controllable Ramsey phase offset further positions this high-slope response at a chosen operating point, making the local enhancement accessible without requiring a large signal-induced phase. More broadly, these results establish a multilevel Ramsey route to enhanced phase sensitivity in quantum platforms, in which two signal-collecting internal paths interfere to produce a controllable noncyclic geometric response.
arXiv:2607.08932v1 Announce Type: new
Abstract: Figure-ground organization in the human visual system relies on several shape-based cues, including surroundedness, convexity, and symmetry. While these cues have been extensively studied using abstract stimuli, little is known about how they operate under natural conditions or how they arise from the statistics of natural scenes. Deep neural networks offer a promising path forward: a model that relies on the same figure-ground cues as humans would provide tractable experimental access to the underlying mechanisms. In this study, we evaluate shape-based figure-ground organization in Vision Transformers (ViTs), for which prior work has demonstrated the emergence of object-based grouping. We test 25 ViTs spanning supervised and self-supervised training objectives, by fitting linear probes to predict figure-ground assignment from intermediate patch representations using both natural images and controlled artificial stimuli that isolate individual cues. Our results show that ViTs robustly encode surroundedness and convexity, and that probes trained on natural images generalize zero-shot to artificial stimuli across several models. For symmetry we observe mixed results: the cue is encoded for uniformly colored but not for textured regions. Taken together, our findings demonstrate that Gestalt-like figure-ground cues can be learned from natural scene statistics and position ViTs as a compelling model system for studying the computational mechanisms of perceptual organization.
Code and data is available at https://github.com/mtangemann/mlvbench
arXiv:2607.08934v1 Announce Type: new
Abstract: We construct optimized summation-by-parts (SBP) operators for general function spaces with provably minimal degrees of freedom on open, closed, and half-open nodal distributions. These operators rely on generalized Gaussian quadrature rules, for which we present an improved algorithm that is flexible, efficient, and provably convergent. In cases where free parameters are available, we further introduce two operator optimization strategies. We test our operators on a handful of numerical examples that contain large or unbounded gradients, in which some a priori knowledge of the solution has been assumed to select an appropriate basis. The novel operators are found to outperform standard polynomial operators by several orders of magnitude in solution accuracy relative to degrees of freedom. Furthermore, our novel operators significantly outperform function-space SBP operators with equispaced nodal distributions, which require significantly more nodes for the same operator basis. Finally, we demonstrate that the operator optimization procedures are critical to achieving accurate and efficient discretizations, as the standard SBP construction procedure can lead to nullspace-inconsistent and poorly-conditioned operators.
arXiv:2607.09503v1 Announce Type: new
Abstract: A fundamental challenge in 3D reconstruction and robotic localization is co-visibility: determining which image pairs share overlapping visible surfaces, particularly in scenarios with minimal overlap. We demonstrate that VGGT implicitly encodes co-visibility as an emergent behavior: without any supervision for this task, its internal representations exhibit a clear hierarchical structure mirroring that of large language models, i.e. early layers build a 3D-aware scene representation, while late layers act as dedicated co-visibility reasoners. In particular, we identify layer L17 as a negative anchor that consistently routes non-co-visible pairs for this backbone, regardless of the evaluation setting, providing task-grounded evidence of layer specialization in a geometry-grounded foundation model. Building on this, we introduce Co-VGGT, which freezes VGGT and trains only a lightweight layer-wise mixture-of-experts head (less than 7.5M parameters) to classify co-visibility from RGB alone, treating each layer as a specialized expert whose geometric abstraction is adaptively weighted per input pair. On the Co-VisiON benchmark, Co-VGGT surpasses the human annotation baseline and improves over prior work by more than 25% pairwise and 10% multiview. Pairwise predictions are well-calibrated (ECE=0.030), enabling direct use as edge weights in visibility graphs for downstream SfM and SLAM pipelines without post-hoc correction. Code and data are available.
arXiv:2607.09345v1 Announce Type: new
Abstract: Flaky tests pass and fail on the same code version, weakening the signal of test results and disrupting continuous integration (CI) pipelines. Code-based flakiness detectors report strong benchmark results, yet their use in practice remains limited. We argue that the field is studying the wrong problem: Flakiness is not a static property of test code, which often lacks the information needed to decide whether a test is flaky. Analyzing three code-based detectors operating on test code, we found that widely used benchmarks contain shortcuts that inflate reported F1 scores and that evaluation protocols overstate generalizability. To control for these shortcuts, we curated two datasets. The first, C-IDoFT (54,468 unit tests from 57 GitHub projects), keeps a developer-confirmed subset of IDoFT's flaky tests and rebuilds only the non-flaky class from repeated executions instead of fixed versions of flaky tests. C-IDoFT is a controlled counterfactual, not a benchmark for reuse. Our CodeBERT reimplementations of two published detectors scored far above its constant baselines under the published cross-validation protocol but no better than them once projects were separated. The high scores rested on the labeling shortcut and the evaluation protocol, not on the test code. On FlakeBench, a benchmark restricted to flakiness types typically recognizable from test code, and the same project-disjoint protocol, the models identified nearly all flaky tests. The second dataset, mined from CI logs, contains 86 flaky end-to-end tests that passed and failed on the same commit. The test code and CI log yielded a cause for 42% of them; the other 58% required further execution evidence. Rather than abandoning flakiness prediction, we reframe it around whether an observed failure is flaky and how likely a test is to fail given its execution environment. Our datasets and CI-mining method support this direction.
arXiv:2607.09541v1 Announce Type: new
Abstract: The transition to post-quantum cryptography (PQC) is driving demand for implementations that can meet the computational requirements of real-world applications. Among the proposed PQC constructions, Learning With Errors (LWE) based key encapsulation mechanisms (KEMs) are particularly attractive due to their strong security foundations, but they incur substantial computational costs from matrix operations and large-scale cryptographically secure random number generation. These characteristics position GPU acceleration as an effective approach for lowering the computational overhead of lattice based cryptographic schemes. In this work, we present a portable GPU implementation of a plain LWE based KEM using OpenMP Target offloading. Unlike most existing GPU implementations, which rely on CUDA specific optimizations, our approach uses a single source code base that executes on both NVIDIA and AMD accelerators. We evaluate the proposed implementation on different accelerator architectures, analyzing performance benchmarking, runtime profiling, scalability analysis, and energy to solution measurements. Experimental results show that OpenMP Target offloading delivers substantial acceleration over a multicore CPU baseline while preserving source level portability across heterogeneous GPU ecosystems. Cross platform analysis identifies NVIDIA GH200 and AMD MI300X as the most effective platforms for this memory bound workload, while profiling indicates that memory system organization and CPU GPU interaction play a more critical role than peak compute capability alone. These findings demonstrate that portable GPU acceleration can significantly reduce the computational overhead of PQC while avoiding vendor lock in, thereby facilitating the deployment of quantum resistant cryptographic infrastructures.
arXiv:2607.09544v1 Announce Type: new
Abstract: Despite strong performance on many multimodal tasks, vision-language models (VLMs) still struggle with basic object counting. We investigate whether this reflects missing internal knowledge or a gap between internal representations and verbalized outputs. Training simple probes on activations from four VLMs across five counting datasets reveals that nonlinear probes can reliably detect counting errors, suggesting that VLMs often encode the correct count even when they output the wrong answer. SVCCA analysis shows that probes trained on ground-truth counts and probes trained on model outputs occupy a partially shared activation subspace but read out along misaligned directions. We further validate our findings using a causal steering intervention, proving that strengthening the direction of count-identified probes does improve model counting performance. Motivated by this result, we propose a detector-guided self-correction method that selectively re-prompts the model only when an internal error detector predicts failure. This simple inference-time intervention improves counting accuracy by up to 15.6 absolute percentage points, without any parameter updates. Our results establish activation-based error probing as both a practical tool for improving VLM counting and a mechanistic lens on the gap between internal knowledge and model outputs.
arXiv:2607.09372v1 Announce Type: new
Abstract: Graph Neural Networks (GNNs) provide a learning-based framework for approximating graph quantities that are expensive to compute exactly. This paper investigates GNNs for scalable approximation of betweenness and closeness centrality, formulated as a node-ranking problem. Exact centrality values are used as supervision, and ranking quality is evaluated using Kendall's tau rank correlation. We study whether message-passing GNNs can learn transferable structural representations across different graph topologies rather than only fitting the distribution used during training. On unseen Erdos renyi graphs, the proposed models achieve tau = 0.851 for betweenness and tau = 0.894 for closeness. A large-scale betweenness model trained on graphs with N = 5,000 nodes achieves tau = 0.938, demonstrating scalability. Mixed-distribution training on Erdos renyi, Barabasi-Albert, and Gaussian Random Partition graphs improves betweenness transfer across graph families. In contrast, closeness centrality remains more sensitive to community-structured graphs and shows reduced transfer to real-world topologies. Finally, GNN inference achieves up to a 97.7x speedup over exact computation. These results show that mixed-distribution training can improve structural transfer in GNN-based centrality approximation, while identifying closeness centrality's sensitivity to topology as an open challenge.
arXiv:2607.09558v1 Announce Type: new
Abstract: In this paper, we solve the reachability problem for branching vector addition systems (BVAS), a long standing open problem. Our approach is based on semilinear inductive invariants. More precisely, we prove that if a configuration of a BVAS is not reachable, then there exists an inductive invariant, given as a semilinear set, that does not contain this configuration. Based on this property, we deduce a very simple (enumerative) algorithm solving the reachability problem for BVAS.
arXiv:2607.09383v1 Announce Type: new
Abstract: Substructural type systems give strong static control over aliasing. Examples include uniqueness, separation, and borrowing. How can such control be brought to established languages whose programming models rely on higher-order abstraction, unrestricted aliasing, and pervasive sharing? We study this problem in the context of Scala. We show how to retrofit these guarantees selectively instead of globally: ordinary code keeps Scala's usual aliasing discipline, while stronger guarantees can be enforced where they matter.
Our starting point is Scala's capture checking, whose treatment of capabilities is inspired by the object-capability tradition: capabilities are ordinary values, and capture sets record, in a value's type, which capabilities the value may use. We develop System Capybara, which adds a selective alias-control layer to this mechanism. By tracking separation, consumption, freshness, and read-only access for capabilities, Capybara recovers key reasoning principles from substructural and ownership-based disciplines without global invariants.
We give a type-preserving translation from the surface calculus Capybara to CoreCapybara, a core calculus extending System Capless, the earlier foundation for capture checking. The translation uses quantifiers for capture polymorphism and freshness, and constraint-indexed modal types for separation. We prove a semantic soundness result for the core calculus in Lean 4 and derive type safety, memory safety (no use-after-free or double-free), immutability of read-only computations, and data-race freedom for well-typed programs.
Finally, we implement Scala 3's new separation checker, which brings higher-order separation reasoning about effects, capabilities, and resources to ordinary Scala, including fearless concurrency.
STEEL: Sparsity-Aware Fused Attention for Energy-Efficient Long-Sequence Inference on AMD's XDNA NPU
arXiv:2607.09385v1 Announce Type: new
Abstract: The growing adoption of large language model-based agents within operating system workflows has increased the importance of energy-efficient inference on laptop-class systems-on-chip (SoCs). While cloud offloading remains common, it introduces reliability and privacy concerns that are particularly problematic for agentic workloads. Recent laptop SoCs, therefore, incorporate neural processing engines (NPUs) optimized for energy efficiency; however, effectively mapping attention mechanisms onto NPUs remains challenging due to architectural diversity and explicit data-movement programming models. In this work, we present STEEL, the first open-source implementation of FlashAttention targeting XDNA-like NPUs. STEEL introduces a dataflow formulation of prefill attention, enabling efficient exploitation of spatial parallelism and on-chip memory. Furthermore, STEEL addresses the load imbalance induced by the causal mask by leveraging a sparsity-aware pipeline placement onto the NPU array, reducing synchronization overhead and improving utilization. We evaluate STEEL on the AMD Ryzen AI 9 HX 370 SoC and compare its performance against optimized CPU and GPU implementations. Experimental results show that STEEL reduces energy consumption by an average of 9.17x and 1.75x relative to CPU and GPU baselines, respectively. On XDNA 1, STEEL achieves an average 9.6x latency reduction over the prior state of the art, and delivers a 22.8x speedup on average compared to a layer-by-layer attention implementation on XDNA 2.
arXiv:2607.09146v1 Announce Type: new
Abstract: In recent years, Large Language Models (LLMs) have made significant strides, leading to the emergence of multimodal LLMs capable of processing diverse inputs such as images and audio. Previous research indicates that the supply of multimodal LLMs with combined textual and visual information improves the automatic code generation capabilities. In software development, diagrams such as flowcharts are widely employed to facilitate tasks like code comprehension. While existing studies investigated the impact of visual inputs on LLMs and the usage of software diagrams, the potential influence of providing flowcharts on multimodal LLM performance remains underexplored. In this study, we generated flowcharts from example solution code for AtCoder problems and provided these visual aids alongside problem statements to GPT-4o for code generation. Our findings demonstrate that integrating flowcharts with problem statements yields performance improvements of up to 10%. Furthermore, when employing abstracted flowcharts, we observed a trend indicating that increasing levels of flowchart detail correlate with enhanced performance. Additionally, we compared the effectiveness of flowchart provision to Few-Shot Learning approaches. The findings suggest that one-shot learning provides sustainable improvements, whereas two-shot learning results in only minor improvements. Our work highlights the importance of software diagrams in supporting multimodal LLM-driven code generation.
arXiv:2607.08938v1 Announce Type: new
Abstract: Frontier LLM agents are automating many business tasks, but their high inference cost makes large-scale deployment unsustainable. Small language models (SLMs) offer a cheaper alternative, yet they typically fall short when swapped into a harness designed for a frontier LLM. We show that for many routine business tasks, SLM agents can match LLM performance at 90% lower cost, when paired with an adapted harness that can be automatically discovered by a meta agent. The key insight is that much of the task difficulty is shared across instances and can be lifted from the model into the harness via tailored instructions, tools, and orchestration loops. To study this systematically, we create a framework that maps agent failure modes to harness adaptation strategies, and build a harness optimizer that automatically discovers effective adaptations from failure trajectories. Across seven business-oriented agentic tasks and three SLM families, we found optimized harnesses significantly improve performance on 16 of 21 task-SLM pairs, with seven pairs closing the SLM-LLM performance gap and the best SLM agent recovering 89.7% of LLM performance at 4% of the cost. Our analysis further shows that adaptation works best for tasks with more repetitive workflows and for SLMs with sufficient base capabilities. Together, these results suggest that harness adaptation can expand the practical deployment range of SLM agents in routine business tasks.
arXiv:2607.09409v1 Announce Type: new
Abstract: Declining fertility is one of the defining policy questions of the next decade, and increasingly, what policymakers know about it is shaped by AI-synthesising the evidence base. But ask such a tool about reproduction and the answer depends on the word you use. The same phenomenon, framed clinically (e.g, infertility, IVF) or socially (e.g., childlessness, fertility intentions), is catalogued with radically different completeness. And the catalogue as much, if not more than the underlying scholarship, is what AI synthesis begins with (Bola\~nos et al., 2024). That databases under-index the social sciences, books, and grey literature is well established (Visser et al., 2021). What is new here is holding the topic fixed and asking whether metadata gaps act as a hidden policy filter on a single contested issue: the determinants of (in)fertility.
We use two OpenAlex queries on the same phenomenon: a clinical basket (infertility, subfertility, ART, IVF, fecundity; n=101,645) and a social basket (childlessness, social infertility, fertility intentions, reproductive decision-making; n=3,646). We compare them on metadata completeness, open access, output type, and institutional provenance. The social framing is consistently less machine-legible: output skewed to books and dissertations, authorship university -- rather than healthcare-based. Open access rates are essentially equal (43.1% vs 41.3%), so the gap is in indexing depth, not paywalls, suggesting simple OA mandates will not fix it. On this same mixed literature, even before any coverage bias enters the picture, LLM tools already miss more than they catch when asked to extract hypotheses and claims (Uprety et al., 2025); the bias documented here compounds an already-imperfect extraction stage.
arXiv:2607.09396v1 Announce Type: new
Abstract: We employ large-scale density matrix renormalization group calculations to study the quantum phases of dipolar molecules confined in bent (zigzag) endofullerene chains, as a function of the chain angle $\gamma$. For LiF, ferroelectric order persists across the full range $60^\circ < \gamma 180^\circ$, with the critical effective dipole moment increasing as the chain bends and parallel alignment becomes less favorable. Near the equilateral configuration ($\gamma = 60^\circ$), geometric frustration drives a transition to an antiferroelectric N\'eel-ordered phase in which neighboring dipoles anti-align along the chain axis. We show that capturing this reorientation requires including dipolar couplings beyond the nearest-neighbor approximation, since next-nearest-neighbor interactions become equally strong at $\gamma = 60^\circ$. For confined water, o-D$_2$O reproduces both ordered phases, whereas p-H$_2$O -- owing to its large rotational constants -- develops no order at any chain angle despite the enhanced coordination of the bent geometry. Because a zigzag chain is the narrowest stripe of a two-dimensional lattice, these results suggest that engineered endofullerene layers could host a rich variety of dipole-ordered quantum phases beyond the ferroelectric ordering observed in previous work.
arXiv:2607.09410v1 Announce Type: new
Abstract: In this work, we initiate the study of strongly refuting the satisfiability of random ordering constraint satisfaction problems. We show that there is a polynomial-time $\varepsilon$-refutation algorithm for random ordering CSP with predicate $P$ when the number of clauses is above the threshold $\tilde{\Omega}\left(n^{d/2}/\varepsilon^2\right)$, where $d$ is the coordinate degree of the predicate $P$. We further give a smooth three-way tradeoff between the running time, the clause density, and the refutation strength $\varepsilon$ using the Kikuchi method. Finally, we complement our algorithmic results with a computational lower bound based on the class of low coordinate degree algorithms, providing evidence that the established three-way tradeoff is near optimal.
arXiv:2607.09400v1 Announce Type: new
Abstract: Adaptive power management in Electric Vehicles (EVs) requires accurate power prediction. Although deep learning models have emerged as highly effective for time-series forecasting in this domain, their performance is prone to degradation when exposed to data with distributions different from the training data. We introduce a novel approach that enables on-device learning in resource-constrained EV systems to continuously adapt pretrained battery prediction models to new, unseen data. We leverage existing pretrained models by transforming them into adaptable versions that retain critical hyperparameter knowledge from their initial training. We comprehensively investigate both online and offline model adaptation strategies. Our results demonstrate significant improvements in forecasting performance across various models and time horizons, achieving mean absolute error reductions of up to 7.49\% and 14.88\% with online and offline adaptation techniques, respectively. This study highlights the substantial benefit of on-device adaptation, resulting in enhanced battery power predictions than unadapted model deployments in real-world EV scenarios.
arXiv:2607.09401v1 Announce Type: new
Abstract: Surface relaxivity and time-dependent diffusion are two readouts of the same wall collisions on one substrate: the transverse rate that microstructure imaging fits is a bulk rate plus a surface rate $\rho\,(S/V)$, and intra- and extra-axonal water carry different $S/V$, so their $T_2$ differ and any compartment estimate that normalises by a TE-weighted $b=0$ is biased. We derive closed forms for the interior (Brownstein-Tarr) and exterior (Novikov-Burcaw) surface rates over myelinated cylinders, validate them with wall-counting Monte Carlo, and quantify the resulting bias on the diffusion intra-axonal signal fraction $f_{\mathrm{intra}}$ and on the myelin water fraction (MWF). The interior/exterior $S/V$ ratio is $(1-\mathrm{VF})/(g\,\mathrm{VF})$, independent of the calibre distribution and crossing unity at fibre volume fraction $\mathrm{VF}^{\ast}=1/(1+g)$. Physiological white matter sits above this crossover, so surface relaxivity over-weights the intra-axonal signal, biasing $f_{\mathrm{intra}}$ by $\approx 12\%$ at clinical PGSE ($\mathrm{TE}=80\,\mathrm{ms}$). That fractions are TE-dependent is known; new here are the surface-relaxivity and packing attribution, a closed-form sign law, and a testable packing-dependent TE drift. The same physics reads through relaxometry as a smaller MWF bias: the thinnest axons' water crosses below the myelin window and is counted as myelin, so fine white matter reads myelin-richer ($\sim 0.33\,\mathrm{pp}$, beneath single-voxel noise but super-linear in the uncertain $\rho$), while in primary lumen-preserving demyelination this bias is nearly constant and cancels in the longitudinal change. One $S/V$ physics thus biases both diffusion and relaxometry microstructure estimates, the $f_{\mathrm{intra}}$ bias being first-order and packing-dependent, the MWF bias small and structural.
arXiv:2607.09422v1 Announce Type: new
Abstract: Cooperative multi-agent reinforcement learning is well suited to problems with large parameter spaces and exploitable local structure, such as the tuning of electrostatically-defined quantum-dot arrays. However, if parameter cross-talk is strong, a non-stationary environment from the perspective of any individual agent can destabilize learning - the same effect that plagues manual tuning of such systems. We propose using a factored representation of the action space, learned online, to decouple agents and minimize their interference. Our framework, QADAPT, uses this factorization to efficiently learn shared policies based on local measurements and rewards. With this modular strategy, we achieve zero-shot generalization to unseen quantum device sizes and maintain an approximately constant number of convergence steps to reach target regimes. This work provides a scalable route toward the rapid calibration of large-scale quantum processors.
arXiv:2607.09415v1 Announce Type: new
Abstract: Long-context processing has become increasingly important for large language models (LLMs), but simply extending the context window does not guarantee effective utilization of long inputs. As input length grows, accuracy often degrades, indicating that models still struggle to identify and use the evidence most relevant to a question. A promising way to improve long-context utilization is test-time training (TTT), which treats the test context as a training example for instance-specific parameter adaptation. However, applying TTT to the entire long context is prohibitively expensive, while adapting on randomly sampled spans introduces severe noise. Because most spans in a long context are irrelevant to the specific question, training on them may even degrade the base model's performance. Our preliminary study shows that TTT is highly sensitive to training-span quality: on LongBench-v2, TTT on randomly sampled spans hurts performance, whereas TTT on oracle spans substantially improves it. Motivated by this, we propose a simple method, Self-Guided TTT (S-TTT): before adaptation, the model identifies the evidence spans it should learn from, and the standard language-modeling training objective is applied only to those selected spans. On two challenging long-context reasoning benchmarks, LongBench-v2 and LongBench-Pro, S-TTT improves accuracy for both Qwen3-4B-Thinking-2507 and Llama-3.1-8B-Instruct, achieving up to a 15% relative improvement.
arXiv:2607.09553v1 Announce Type: new
Abstract: Software development is increasingly moving toward agentic-first workflows. This includes AI agents responsible for generating initial fixes for submitted issue reports. In this setting, issue reports are no longer merely documentation for human maintainers; they become the primary task specification for the agent. However, little is known about how such reports should be written to maximize the agent's chances of producing a correct fix. We study what makes a bug report agent-ready. Starting from the SWE-bench Verified benchmark (i.e., a collection of 500 real repository issues with human-written gold patches and test suites for evaluating generated fixes) we manually classify each issue by change type (e.g., bug fix vs refactoring) and annotate each sentence with its information type, such as observed behavior, expected behavior, reproduction steps, localization cues, and suggested fixes. We focus on the 441 issues representing bug reports, and we run on them mini-swe-agent using three LLM backbones (i.e., GPT-5-mini, MiniMax M2.5, and Gemini 3 Flash). We then fit a binomial regression model to estimate the incremental association between each information type and agent success, controlling for confounding factors. Our results suggest that agentic-first reports benefit most from information that narrows the agent's search and repair space. Localization cues, such as references to affected code areas, are positively associated with successful repairs, while suggested fixes, expressed either in code or natural language, show some of the strongest positive associations with pass probability. An ablation study removing selected information types confirms that agents benefit less from information traditionally useful to humans, such as reproduction steps, and more from sentences that expose a repair direction, either through bug localization or a suggested fix.
arXiv:2607.09523v1 Announce Type: new
Abstract: Epilepsy is one of the most common neurological conditions, and people living with epilepsy (PLWE) often use social media as a resource. However, a comprehensive understanding of the topics represented in epilepsy-specific communities where PLWE may be more honest is essential to designing better technologies to address epilepsy self-management. To understand the main topics and concerns of PLWE, we collected 23,944 r/Epilepsy subreddit posts and performed topic modeling, thematic, and psycho-linguistic analyses. We found five major themes for those topics: symptoms and triggers (e.g., mental health and memory, sleep/nocturnal, and photosensitivity), treatment and healthcare experience (e.g., medication, understanding epilepsy), daily functions ( e.g., perceived level of independence and finances), seizure activity (e.g., auras and ictal symptoms), and support for PLWE (assisting PLWE and support for PLWE). We highlight the top psycho-linguistic characteristics of posts across different topics. Our contributions include providing an understanding of the challenges of an online epilepsy community and their social support needs, and implications for designing technologies.