Science & Mathematics

Coding agents increasingly undertake development tasks in mature enterprise repositories, yet existing benchmarks offer limited insight into whether they fulfill complete Product Requirement Documents (PRDs) and where delivery breaks down. Issue resolution and development from scratch leave incremental PRD fulfillment underexplored, while isolated tests provide incomplete evidence of business and integration requirements. We introduce PACE, a benchmark of 43 enterprise tasks with 750 expert-audited Code Points. It combines requirement reconstruction, criterion construction, and evidence-based evaluation to measure PRD fulfillment and connect delivery gaps to agent behavior. Across 11 model--scaffold configurations, most scores fall between 52.1 and 78.1; GPT-5.6-Sol achieves the highest complete-delivery rate of 14.0\%. Incomplete implementation and missing integration are the principal bottlenecks: producing individual components does not ensure a complete delivery. Evaluation achieves 95.2\% agreement with expert consensus, with lower case-level variation across judging records than repeated executions. Beyond model comparison, the benchmark provides actionable feedback for harness refinement: diagnosis guides targeted improvements, and successive harness releases raise mean performance from 59.4 to 63.0 using the same model and task suite. Code is available at https://github.com/TianqiLiu1/PACE-Bench.

Animals build cognitive maps of vast environments, use them to perform map-based navigation, and might even be reusing the same circuitry used in spatial reasoning for creating and using maps of arbitrary task-spaces. In contrast, current embodied AI systems either rely on hand-engineered mapping pipelines, which break in the presence of unmodeled dynamics and are restricted to metric space, or on learned policies that rarely form map-like representations at all. Developing architectures that could learn to build and use map-like representations as flexibly as animals is, however, difficult because few embodied AI benchmarks actually require map-based reasoning. To address this, we present ForagerSim --- an interdisciplinary research platform and simulation environment focused on embodied tasks that require map-based reasoning, with scenarios ranging from mobile-robot search and rescue to animal foraging in both complex mazes and vast, potentially infinite open areas. ForagerSim is highly modular and allows modifying independent axes of difficulty related to perception (abstract 2D sensing to full RGB or LiDAR), scale (room-scale to multi-km scale), topology (open-fields to complex mazes), dynamics (smoke, debris, sensorimotor failures and others) and tasks (from simple 2D coverage to foraging-inspired well-alternation tasks). We compare reinforcement learning baselines with a simple engineered robotics stack and a human player on 8 example worlds which show how different difficulty axes affect existing methods. Our results show that, when sensing is simplified to 2D, learned policies can perform multi-object search and well-alternation tasks surprisingly well in simple topologies, but they fall short of human performance in larger or more complex-topology environments where map-based baselines perform well, suggesting that map-based reasoning is the bottleneck in these scenarios.

Peres's iteration of von Neumann's procedure, run to exhaustion and keeping everything it does not extract, is a bijection of {0,1}^N that splits every string into fair bits and a residual. The residual is self-delimiting and determines the number of ones, so the fair bits are exactly uniform given the residual under every exchangeable law. Each residual one accounts for a power of two of the weight of the input; consequently, on every input of length N, the residual has fewer than sqrt(3) N^0.793 ones. This deterministic sparsity gives a short proof, with an explicit remainder, that the number of fair bits per symbol tends almost surely to the entropy, for Bernoulli sources and, by binarisation, on every finite alphabet. For coding, the Bernoulli likelihood factorises along the recursion, and code B, which stores the fair bits and codes the residual at plug-in biases, has the length of the two-part code. Alternatively, code G stores the fair bits and writes the Elias gamma codes of the gaps of the residual: it needs no parameter, no estimate and no arithmetic coder. On every string its length is at most the logarithm of the type class plus O(N^0.793 log N); a concatenation of J pieces costs, without transmitting the cuts, its piecewise empirical entropy plus a term that is sublinear when J = o(N). After a Burrows-Wheeler transform of the reversed string, code G competes on every individual sequence with the empirical conditional entropy of every order at once, which gives universality for stationary ergodic sources.

On-policy distillation can transfer the reasoning capabilities of a strong teacher to a smaller student. However, distillation from a stronger teacher model faces two mismatches: its solutions may be difficult for a smaller student to imitate, and its token-level probabilities may be incompatible with, or even unavailable to, the student. Existing methods handle these settings through different forms of supervision. To this end, we propose Bridged Self-Distillation (BSD), a unified framework for distilling from any teacher that provides reasoning traces. The model to be distilled from, termed the external teacher, first provides a reasoning trace as privileged context, which can be generated in an offline fashion. The student generates rollouts without this context, while a copy of the student conditioned on it, serving as the self-teacher, scores the same rollouts in the student's own token space. BSD uses the resulting token-level guidance to adjust updates on unsuccessful rollouts, with a verifier providing the outcome signal. As the external teacher is queried only to produce text, the same training procedure applies to all teacher scenarios: shared-tokenizer, cross-tokenizer, and closed API teachers. Across these scenarios, BSD improves over the base model and outperforms baselines tailored to each scenario, offering a novel and unified pathway for distillation. Code is available: https://github.com/TUMLegalTech/Bridged-Self-Distillation

Multi-hop knowledge-graph question answering (KGQA) benchmarks are often treated as fixed language-and-reasoning tasks, but for navigation-based methods, difficulty also depends on the graph in which those tasks are instantiated. This makes graph construction a consequential but often entangled part of the navigation task. To isolate this effect, we audit PathQuestion (PQ) and PathQuestion-Large (PQL) as graph-navigation benchmarks under a controlled intervention: we hold questions, topic entities, released answers, released path annotations, and family-level splits fixed while replacing each released task-filtered graph with the larger source KG distributed with the benchmark. Source restoration expands the effective navigation search space and graph-consistent answer and path multiplicity while sharply reducing unguided reachability to released answers. We evaluate whether these structural changes persist across navigation policies using structural references, frozen small-language-model navigation policies, and trained MINERVA policies. Across both frozen and trained policies, restoration substantially reduces released-answer accuracy and trajectory fidelity, while graph-consistent evaluation recovers only part of the loss. These results show that benchmark graph construction is part of the navigation task specification rather than merely an implementation detail and can materially shape measured behavior even when the released QA task is held fixed.

Document understanding is increasingly judged on where an answer comes from, scored against annotated gold locations as if each fact lived in one place. In long visual documents, a fact often lives in several places: a chart value is restated in the text, a key figure reappears in the summary. We ask how often such copies occur in standard benchmarks and what they do to evaluation. We annotate 420 questions from MMLongBench-Doc and MMDocRAG with the locations that independently establish each required fact, checked by two independent reviewers. Copies are common and often cross-modal: 19.5% of MMLongBench-Doc test questions have a strict copy outside the gold, and in 28% of MMDocRAG questions a non-gold candidate alone supplies a required fact. Single-gold scoring then fails in three predictable ways. A retriever that finds a copy is scored as a miss: page recall@1 of text and visual retrievers is underestimated by 13-24 points on questions with a cross-page copy. A model that cites a copy is scored as citing wrongly: crediting equivalent evidence reverses 11% of pairwise orderings among 65 competitive MMDocRAG systems. Masking the gold region leaves the fact available elsewhere: with a copy in the input, 41-56% of correct answers across four models survive it yet fail once every annotated instance is masked. Set-level masking with automatically found copies closes 78% of the gap to masking every annotated instance at matched granularity (three models); an unverified key-string rule lowers survival at least as far but masks three times the area. We release the annotations and redundancy-aware metrics.

This study presents a 96-day longitudinal analysis of Secure Reliable Transport (SRT) protocol behavior in a production streaming environment between Iraq and Turkey, examining 262,672 transport metric samples and 5,033,104 system resource measurements collected between October 2025 and January 2026. The research addresses temporal patterns, system-resource relationships, attack vulnerability, and anomaly detection. Results reveal significant temporal variation: Negative Acknowledgment exhibits 7.6-fold hourly variation and 11.5-fold daily variation (p < 0.001), though with small effect sizes (eta^2 < 0.04), indicating that temporal period explains less than 4 percent of total variance. Central Processing Unit (CPU) utilization emerges as the strongest system-level correlate of transport variation (r = 0.617 with NAK rate). Controlled User Datagram Protocol (UDP) flood experiments at 500 packets per second cause CPU increases of 176 to 806 percent, while SRT transport metrics change by only plus or minus 13 percent demonstrating protocol resilience through the Automatic Repeat Request mechanism. An unexpected anomaly reaching 97.7 percent CPU enabled signature-based differentiation between attack types. A Z-score anomaly detection method outperforms fixed thresholds, detecting all 7 security events. This study contributes the longest temporal characterization of SRT metrics reported to date, the first system-SRT correlation analysis, and the first controlled security assessment of SRT infrastructure. These findings provide actionable guidance for capacity planning, security hardening, and multi-metric monitoring of production SRT deployments.

Referring Expression Comprehension (REC) localizes the image region described by a natural language expression, underpinning applications such as autonomous navigation, human-robot interaction, and medical image analysis. Under weak supervision, REC is challenged by the visual-linguistic semantic gap and the difficulty of inferring region-text correspondences from coarse image-level signals alone. Moreover, no single visual encoder captures all complementary properties---semantic alignment, fine-grained texture, and boundary awareness---while naively fusing multiple pretrained encoders yields heterogeneous, hard-to-reconcile representations. We propose LaMER, a weakly supervised REC framework that dynamically aggregates four frozen expert encoders through a language-conditioned Cross-Modal Routing Fusion module, adapting expert selection to each referring expression without instance-level bounding-box supervision. A Cell-Text Matching Head with Negative Anchor Augmentation, trained with a multi-objective loss combining contrastive, reconstruction, alignment, and load-balancing terms, further stabilizes optimization. LaMER achieves 77.67\%, 62.81\%, and 69.49\% Acc@IoU\(\geq 0.5\) on RefCOCO, RefCOCO+, and RefCOCOg, surpassing DViN and WeakMCN by up to 16.49 and 14.49 points, with ablations validating each component.

oollim is a locally executable system for evaluating L2 English pronunciation accuracy, fluency, and prosody on a consumer computer. The system combines pretrained XLS-R, XEUS, and PhoneticXeus representations with reference-text features and ridge regressions trained on SpeechOcean762, the Korean Learners' L2 Speech Corpus (English), J-AESOP, and their combinations. On 305 independently held-out recordings assessed by three expert raters, several ridge configurations substantially outperform archival Microsoft Azure Pronunciation Assessment API data in both Spearman correlation and quadratic weighted kappa across all three constructs. These results support expert speech evaluation on consumer hardware using pretrained representations and construct-specific regressions, without neural-model fine-tuning or calibration on the private evaluation data.

This paper considers the Bayesian top-$k$ identification problem, wherein a decision maker is tasked with selecting $k$ items from a pool of items of unknown quality. To assess the items, the decision maker adaptively allocates a fixed sampling budget, where each sample of an item yields a noisy observation of its quality, with the objective of minimizing the expected opportunity cost of the selected set. We propose an approach that allocates each batch of samples by minimizing a surrogate objective derived from an upper bound on the expected opportunity cost. The approach is general: samples can be allocated in batches, the selection size $k$ is arbitrary, and it applies to many sampling distributions. It is also simple and intuitive. We prove that the method's posterior expected opportunity cost decays at an exponential rate of at least half the best rate achievable by any sampling rule. In simulations across a wide range of settings, the method is competitive with, and often improves upon, the best existing algorithm in each setting.

Image-based spatial reasoning requires perception, decomposition, and inference, but the stage that limits accuracy can change during training. Answer-level rewards neither localize failures to individual stages nor provide discriminative group-relative feedback when all rollouts receive the same reward. Process supervision provides intermediate feedback, but fixed weights cannot adapt reward allocation to these changing bottlenecks. To address this gap, we propose CALIPER, an adaptive process-reward framework supported by SpatialTrace, a corpus of stage-decomposed spatial reasoning traces. CALIPER jointly estimates the associations between stage-wise reference coverage and answer correctness through within-group ridge regression, using these associations as utility proxies to gradually update reward weights. The resulting process reward supervises all rollout groups, including uniformly failing ones. Because utility estimates can become unreliable when answer-discriminative groups are scarce, a closed-loop harness monitors estimator reliability and validation performance to guide checkpoint rollback, rollout diversification, and early stopping. Extensive experiments across eight benchmarks and three backbones show that CALIPER achieves the highest mean under both one-shot and harness training, with consistent gains over fixed allocation across RL optimizers. Further analyses confirm the benefits of adaptive reward allocation and harness control, with favorable scaling in model/data size.

Handwritten work captures mathematical reasoning that learners may not express in typed or voice-dictated messages. We built a multimodal AI tutoring platform in which a phone camera gives the tutor a live view of learners' work on paper. In a randomized online study, 117 adults solved calculus problems with Text-Reactive, Visual-Reactive, or Visual-Proactive assistance. Analysis of 2,140 tutor messages showed that visual access shifted assistance toward checking ongoing work, while proactivity enhanced error identification and directive hints. Learners valued feedback grounded in their writing; those who received proactive help rated it as supportive, and about a third wanted it more often. These findings inform when tutors should intervene and what they should address. We release the platform, prompts, and instruments, together with 255,984 timestamped images of handwritten work linked to tutoring conversations, intervention decisions, and learning outcomes, to support multimodal tutoring systems that preserve the affordances of handwritten work while providing adaptive feedback.

Much of the manipulation in influencer content is not false but disproportionate, framing accurate facts as historic, urgent, or catastrophic. Existing approaches either verify factual claims or detect clickbait and propaganda in single-platform, single-language text; however, they overlook where such framing originates, because they treat a post and the replies beneath it as one unit. To address this gap, we measure rhetorical distortion with one instrument applied identically across six platforms and two languages, classifying creator posts and their replies separately. Our three-tier pipeline detects five literature-grounded dimensions (Significance Inflation, Anxiety Manufacturing, Novelty Claims, Loaded Language, and Temporal Distortion): bilingual lexical rules propose candidates, negative filters cancel known false positives, and an LLM verifies or relabels the candidates. This design keeps labels transparent and comparable across platforms, calls the LLM on only 14% of posts, and supports validation against human annotators and a second model. On 13,060 posts from 330 accounts on RSS/newsletters, YouTube, Bluesky, Reddit, Weibo, and X/Twitter, platform is far more strongly associated with account-level distortion than content category (partial $η^2=0.49$ vs. $0.09$), and X/Twitter scores highest (DI 7.74 vs. 0.78 for RSS). On X/Twitter, the top-5 replies under a post carry 3.5x the distortion of the tweet they answer (1.5x at equal length), while creator tweets alone are indistinguishable from YouTube and Bluesky; two human annotators and GPT-4o confirm this gap. Measured distortion on threaded platforms thus lives largely in the audience layer, which calls for measuring creator and reply text separately.

Expressive robot motion can improve naturalness, effectiveness, and perceived likability in human-robot interaction. When deployed on dynamically unstable platforms, however, reactive execution of expressive body motion may bring the robot to its stability limits, especially when expressivity is decoupled from physical control. In this paper, we present RoboMMT (Robot Modulated Motion Tracking), a modular control architecture that couples parameterized expressive motion synthesis, physics-based character control, and reactive adaptation to spatial interaction goals. Our approach trains a reference-conditioned tracking policy across a broad domain of expressivity, enabling it to faithfully realize the commanded motions while maintaining dynamic stability. Goal reactivity is achieved by training lightweight, task-specific interaction policies that modulate the reference generator's spatial goal-attraction point, with the stabilizing tracking policy kept in the loop. Evaluations on a quadruped and two humanoid robots show that the learned tracker remains stable and accurate across the entire modulation domain, whereas trackers trained without this coverage lose balance, tracking accuracy, or both. We show that layering closed-loop goal adaptation over this foundation enables interactive facing and pointing behaviors, while maintaining stable execution and comparable reference-tracking fidelity. The robot learns to proactively redistribute its weight and take corrective steps. Zero-shot sim-to-real transfer is demonstrated through real-robot experiments on a quadruped under heavy disturbances.

Reinforcement learning with verifiable rewards (RLVR) is particularly well suited to code generation because generated programs can be objectively evaluated through execution. Execution also provides behavioral information that can be reused as additional supervision. The Synergistic Policy and Reward Co-Evolving Framework (SPARK), originally proposed for reasoning tasks, exploits this idea by recycling training rollouts and their evaluation results to train a model not only to generate solutions but also to evaluate and improve them. We investigate whether this policy-reward co-evolution approach transfers effectively to code generation and whether execution-grounded reflection provides an additional source of self-improvement. We compare SPARK-style rollout recycling with execution-only Group Relative Policy Optimization (GRPO) using Qwen2.5-Coder-3B-Instruct. Our results do not support either hypothesis under the configuration tested. Although co-evolution improves training performance, these gains do not generalize to held-out problems. Compared at the same stage of training, rollout recycling produces roughly 34x greater KL divergence from the reference policy than execution-only GRPO. Over training, trained tokens per GRPO sequence fall from 185 to 86 as training pass rate increases, while held-out performance declines or fails to sustain early gains. We characterize these patterns as single-adapter policy drift: the shared trainable parameters increasingly depart from pretrained code-generation behavior without corresponding gains in generalization. The results show that higher training reward alone is not evidence of generalizable code self-improvement and highlight the importance of held-out evaluation and drift diagnostics.