Explore the Frontier of Global Academia

01.

arXiv (CS.AI) 2026-06-16 DOI: arXiv:2606.15052

PANDA: An LLM-Enhanced Performance-Driven Analog Design Framework Bridging Design Intent and Layout Generation

Authors:

Haoyi Zhang↗Weijian Fan↗Xiaohan Gao↗Bingyang Liu↗Runsheng Wang↗Yibo Lin↗

arXiv:2606.15052v1 Announce Type: cross Abstract: Traditional design of analog circuits heavily relies on manual interventions across topology, sizing, and layout, with prior automation addressing stages in isolation. In this work, we propose PANDA, an LLM-enhanced framework that bridges high-level design intent to final layout by actively managing cross-stage dependencies through guided topology synthesis, substructure-aware sizing, and constraint-driven layout generation. This shifts automation from algorithm-centric execution to intent-centric co-design, reducing turnaround time from days or weeks to hours while improving design performance.

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02.

arXiv (CS.CL) 2026-06-19 DOI: arXiv:2606.20065

Generative Engine Optimization at Scale: Measuring Brand Visibility Across AI Search Engines

Authors:

Pratyush Kumar↗

People increasingly get answers straight from AI search engines like ChatGPT, Claude, Perplexity, and Gemini rather than scrolling search results. Brands that once focused on search engine optimization (SEO) must now optimize for how these engines represent, cite, and recommend them – a shift variously called Generative Engine Optimization (GEO), Answer Engine Optimization (AEO), and AI Search Visibility. We treat AEO and AI Visibility as part of GEO, and study how to measure brand visibility across AI engines: what they value when they cite a brand, which sources they rely on, and what content large language models surface. The hard case is everyone outside the already-authoritative top brands – SMEs, D2C brands, creators, and early-stage startups. We analyze 100K+ prompt responses across 100+ brands tracked on Ranqo between March and May 2026. First visibility runs form a clear three-tier brand-stature ladder: global household names (e.g., Stripe, Nike) appear in 73% of relevant AI answers on their first run; established mid-market and regional brands (e.g., Olipop, Klaviyo) in 44%; niche and small brands in just 11% – about 30 percentage points per step. When engines cite sources, about 78% go to corporate websites; among non-corporate sources YouTube leads, ahead of Reddit, editorial media, and Wikipedia. The highest-leverage page is the ranked "best-of" listicle, the most-cited content format at about 21% of all citations. Sentiment is the unstable signal: whether a brand is framed positively or negatively flips about 6.7 times more often than whether it is mentioned at all. These findings provide a first large-scale baseline for measuring GEO: AI brand visibility can be measured, differs by platform, and varies strongly by brand maturity. We close by proposing seven v1.1 protocols to test whether specific recommendations can causally improve AI visibility.

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03.

arXiv (CS.CL) 2026-06-16 DOI: arXiv:2606.15821

The Truth Stays in the Family: Enhancing Contextual Grounding via Inherited Truthful Heads in Model Lineages

Authors:

Miso Choi↗Seonga Choi↗Mincheol Kwon↗Woosung Joung↗Jinkyu Kim↗Jungbeom Lee↗

Recent advances in large language models (LLMs) have produced many specialized multimodal LLMs (MLLMs) that share common foundational LLMs, forming distinct model lineages. It remains unclear whether a fundamental behavioral link exists between the foundational LLMs and downstream variants. We investigate this question by quantifying head-level context-truthfulness scores. Across diverse LLM and MLLM lineages, including Vicuna-, Qwen2.5-, LLaMA2-, and Mistral-based models, we find that Truth Scores are strongly preserved within model families, even after instruction tuning or multimodal adaptation. We further show that this inheritance is consistent with attention-head weight preservation, and that context-truthful heads attend to query-relevant evidence. Building on this finding, we propose TruthProbe, a soft-gating strategy that amplifies context-truthful heads while preserving other head contributions. TruthProbe improves contextual truthfulness on HaluEval and reduces multimodal hallucination on POPE and CHAIR, with base-LLM Truth Scores transferring effectively to their fine-tuned LLM and MLLM descendants. Code is available at https://github.com/miso-choi/TruthProbe.

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04.

medRxiv (Medicine) 2026-06-23 DOI: HASH:1179c8614c164c0b59222791d537a450

A pharmacometric grey zone reconciles high metronidazole resistance rates with bismuth quadruple therapy efficacy in Helicobacter pylori

Authors:

He↗Wang↗Xu↗

Summary Background Metronidazole (MET) resistance in Helicobacter pylori (H. pylori) exceeds 50-60% globally, yet MET-containing bismuth quadruple therapy (BQT) achieves &gt90% eradication in MET-resistant infections. We hypothesise this discordance stems from a structural limitation of two-fold dilution: a pharmacometric grey zone between the 128 and 256 &microg/mL breakpoints where treatable isolates are systematically misclassified as high-level resistance. Methods In a real-world cohort of 4610 treatment-na&iumlve children (2019-2024), checkerboard assays determined the bismuth-MET synergy factor (SF). Population PK/PD modelling simulated gastric MET exposure (AUC

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05.

arXiv (math.PR) 2026-06-17 DOI: arXiv:2605.01894

Poisson approximation by coupling

Authors:

Rinaldo B. Schinazi↗

arXiv:2605.01894v2 Announce Type: replace Abstract: It is well known that a binomial $(n,p)$ can be approximated by a Poisson distribution with parameter $np$. The typical approach in undergraduate probability texts is to show a convergence result for the distribution of the binomial as $n$ goes to infinity and $np$ converges to some $\lambda$. In this note we use instead the coupling technique to show a much more general result. Moreover, we only use elementary results from probability.

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06.

medRxiv (Medicine) 2026-06-16 DOI: HASH:c64cf2c9d764c623da3b4d85a95d0a62

Reliability and construct validity of the Technology Device Interference Scale in a sample of children and parents

Authors:

Schumacher↗Tu↗Cost↗K. T↗Baribeau↗Birken↗C. S↗Charach↗Kelley↗Burton↗Maguire↗J. L↗…

There is increasing interest in parent-child technoference: the interference with personal interactions caused by technology devices. This study examined the reliability and construct validity of the Technology Device Interference Scale (TDIS) to measure technoference in a sample of Canadian parents and children. Parents (n=883) and children (n=376) were recruited from clinical and community settings and completed the TDIS for their own and family member technoference over three timepoints (T1=2023, T2=2024, T3=2025). TDIS internal consistency, test-retest reliability, and construct validity were assessed using Cronbachs alpha, intraclass correlation coefficient, and confirmatory factor analysis, respectively. The TDIS showed good internal consistency and adequate to good construct validity when used by children to report on their own technoference (all >.70; CFI>.95, TLI>.95, RMSEA.70; CFI>.95, TLI>.90, RMSEA[≤].11). The TDIS had low to acceptable internal consistency and poor model fit for parent report of their own technoference ( range: .63 - .66; CFI

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07.

arXiv (quant-ph) 2026-06-11 DOI: arXiv:2606.11561

Diffusive Relaxation of Participation Entropy in U(1)-symmetric Dynamics

Authors:

Hanchen Liu↗Tianci Zhou↗Xiao Chen↗

arXiv:2606.11561v1 Announce Type: new Abstract: Participation entropy (PE) quantifies the spread of a many-body wavefunction across configuration space. While PE relaxes rapidly in generic chaotic systems, we show that $\mathrm{U}(1)$ conservation laws slow it down by imprinting with the slow hydrodynamic modes. Using a cluster expansion around equilibrium, we show that, after local density inhomogeneities decay, the leading PE deficit is dominated by squared connected density correlations. The long time relaxation is therefore controlled by diffusive correlation spreading, giving $\Delta S(t)\sim t^{-1/2}$ in the hydrodynamic regime and crossing over to $\sim \exp[-O(t/L^2)]$ when $t\geq L^2$. We confirm this entropy correlation relation using exact computation and infinite system tensor network simulations in various quantum $\mathrm{U}(1)$ conserving circuits. Our results establish PE as a sensitive probe of hydrodynamic memory and suggest that slow relaxation is a generic consequence of conservation laws.

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08.

arXiv (CS.CL) 2026-06-12 DOI: arXiv:2606.13464

Ontology Memory-Augmented ASR Correction for Long Text-Speech Interleaved Conversations

Authors:

Xinxin Li↗Huiyao Chen↗Meishan Zhang↗Yunxin Li↗Zulong Chen↗Zhibo Ren↗Xiaoqing Dong Baotian Hu↗Min Zhang↗

Automatic speech recognition (ASR) correction has traditionally focused on isolated utterances or short local contexts. However, as text and speech become increasingly interleaved in long interactions, ASR correction requires conversation-level contextual evidence. Existing ASR correction methods often rely on the current hypothesis or concatenate raw dialogue history. In such contexts, sparse correction evidence can be difficult to locate amid redundancy and noise. Addressing these challenges, we propose an ontology memory-augmented ASR correction framework for long text-speech interleaved conversations. The framework organizes preceding interaction history into a dynamically updatable ontology memory, where entities, terminology, surface variants, potential ASR confusions, and semantic relations are stored as retrievable nodes for context-grounded correction. To evaluate this setting, we construct RAMC-Corr, a dataset derived from MAGIC-RAMC for long-range ASR correction with grounded context. Experiments on RAMC-Corr show that our method improves over direct correction in 9 out of 10 paired backbone-setting combinations and encourages more selective and evidence-grounded corrections for context-dependent ASR errors.

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09.

arXiv (CS.CL) 2026-06-15 DOI: arXiv:2606.13977

Creative Integration: A Decidable Criterion of Creativity

Authors:

Yoshinori Nomura↗

"Integrative" solutions are widely praised but rarely defined: we lack an operational way to tell a genuine integration – one that makes the world cheaper to describe – from a tidy re-description. Building on the lineage that treats creativity and intelligence as compression, we give such a criterion for creative integration (CI): the resolution of a real conflict between A and B is CI if and only if, under a fixed description language, the description length strictly shrinks (C = L_pre/L_post > 1), with the reduction located in the conflict itself. We make the judgment decidable through four binary, conjunctive gates, and we fix its extension through a taxonomy of pseudo-integration that names and rejects the look-alikes. We back the criterion with a curated, multi-domain corpus and – crucially – validate it not by human inter-rater agreement but by four falsifiable tests it could fail: an independent computational check, discrimination against hard negatives, out-of-sample prediction, and description-language robustness; all pass with margin. The contribution is not "creativity is compression" but its decidability, discrimination, and corpus: on this account, what makes a move genuinely creative – rather than merely novel – is that it compresses a conflict, with novelty and value as downstream symptoms; whether all creativity is so constituted we state as an explicit conjecture. We claim only the sign of C-1; we judge, not generate. The result is a citable primitive for a broader program.

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10.

arXiv (quant-ph) 2026-06-16 DOI: arXiv:2606.15467

Hardy and Cabello Arguments in Spatial and Temporal Frauchiger-Renner Scenarios

Authors:

Ehsan Erfani Maharat↗Ali Ahanj↗Mohsen Sarbishaei↗

arXiv:2606.15467v1 Announce Type: new Abstract: We investigate Hardy- and Cabello-type logical structures within spatial and temporal extensions of the Frauchiger–Renner (FR) framework, embedding these constructions directly into the FR multi-observer architecture. In the spatial multi-observer scenario, both Hardy and Cabello contradictions arise, with the Cabello construction yielding the stronger violation,$\(\Delta_Cabello^{\max}=0.1078\)$, which exceeds the maximal Hardy probability $\(P_{H}^{\max}=\frac{5\sqrt{5}-11}{2}\approx 0.09017\)$. We then develop a sequential temporal FR protocol based on coherent multi-observer measurements performed on a single spin-$\tfrac12$ system. In this temporal setting, the Hardy contradiction disappears identically due to dynamical constraints imposed by sequential state updates, whereas a finite Cabello-type violation survives, \(\Delta_Cabello^{\max}\approx 0.0674\). Our results establish a fundamental structural distinction between spatial entanglement and temporal multi-observer correlations in FR-type logical scenarios, and demonstrate that certain observer-independent description failures persist even without spacelike separation.

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11.

arXiv (CS.AI) 2026-06-16 DOI: arXiv:2606.15766

Rethinking Scaffolding in LLM Tutors: The Interactional Mismatch Between Benchmarks and Real-World Deployments

Authors:

Alexandra Neagu↗Jeffrey T. H. Wong↗Marcus Messer↗Rhodri Nelson↗Peter B. Johnson↗

arXiv:2606.15766v1 Announce Type: new Abstract: A central pedagogical value evaluated in AI tutor benchmarks is scaffolding: guiding students through graduated steps toward a solution. Alignment and evaluation methods for embedding scaffolding behaviour into chatbots, however, rest on an implicit assumption: that students will take up the scaffolding and engage in the conversation. To examine whether this assumption holds, we introduce an evaluation pipeline around two metrics - Chatbot Scaffolding and Student Uptake - and apply them across nine datasets of 9,490 chats, spanning AI tutor benchmarks and real-world deployments of educational chatbots. Our analysis reveals that while benchmarks assume a high-scaffolding, high-student-uptake environment, students in real-world settings exhibit lower levels of uptake overall - frequently bypassing the chatbot's pedagogical framing to drive the interaction toward their own learning goals at little interpersonal cost. We argue that bypassing scaffolding is not necessarily detrimental; rather, it frequently highlights a mismatch between a chatbot's pedagogical framing and the student's learning goals. To meaningfully evaluate the effectiveness of a chatbot's assistance, future benchmarks must move beyond the assumption that students will simply take up the scaffolding, and instead evaluate how these chatbots navigate diverse learning contexts and student-driven interaction patterns.

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12.

arXiv (CS.LG) 2026-06-12 DOI: arXiv:2606.12990

Exposure Bias as Epistemic Underidentification in Recursive Forecasting

Authors:

Riku Green↗Zahraa S. Abdallah↗Telmo M Silva Filho↗

arXiv:2606.12990v1 Announce Type: new Abstract: Recursive multi-step forecasting is usually framed as distribution shift: models are trained on observed histories but deployed on their own predictions. We show this framing is incomplete by proving that, under partial observability or state truncation, recursive rollout is also an epistemic underidentification problem. Even with deterministic latent dynamics, one-step Bayes supervision identifies behavior only on observed contexts and need not identify the deployed recursive predictor once rollout queries self-generated induced states whose correct local targets are not determined by numeric state alone. We formalize this with induced states $Z$ and provenance variables $P$, and derive a decomposition of induced-state error into teacher-forcing/rollout mismatch, representation–class approximation, and provenance information gaps. Empirically, we show that rollout enters a distinct induced-state regime, that fixed induced states define a distinct local corrective task, and that closed-loop gains arise not only from local adaptation but also from changing the induced states visited during rollout. Using a simple binary provenance encoding, provenance-aware correction can further improve performance, though gains are conditional rather than uniform. These results recast exposure bias as reasoning under self-induced epistemic uncertainty.

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13.

arXiv (CS.CV) 2026-06-24 DOI: arXiv:2606.24539

PointVG-R: Internalizing Geometric Reasoning in MLLMs for Precise Pointing Localization via Visual Chain of Thought

Authors:

Ling Li↗Bowen Liu↗Zinuo Zhan↗Jianhui Zhong↗Ziyu Zhu↗Bingcai Wei↗Kenglun Chang↗Zhidong Deng↗

Pointing-based visual grounding requires models to precisely locate target objects by deciphering complex spatial relationships between the visual scene and pointing gestures. Traditional methods typically encode input images into static feature representations and perform reasoning primarily within the linguistic domain, often overlooking the rich perceptual cues and explicit spatial geometry inherent in images. In this study, we aim to mitigate the cognitive vulnerability of models in interpreting gestural spatial relations by proposing PointVG-R, a reasoning-guided Multi-modal Large Language Model (MLLM). PointVG-R introduces geometric-aware reasoning for pointing-based grounding, enabling the model to think with images through the strategic integration of Reinforcement Learning (RL) and cold-start data. Specifically, we design a novel geometric reasoning pipeline that simulates the iterative cognitive process humans employ when interpreting pointing gestures. Furthermore, we construct EgoPoint-CoT, a high-quality visual Chain-of-Thought (CoT) dataset featuring detailed reasoning trajectories to guide the model via Supervised Fine-Tuning (SFT) and RL. To address the varying quality of learning signals encountered during training, we further propose an Adaptive Importance Weighting strategy based on Group Variance, which dynamically adjusts reward signals to optimize the learning process. Experimental results demonstrate that PointVG-R achieves SOTA performance, outperforming the baseline by $15.86$ points in mIoU. Extensive ablation studies further validate the efficacy of our proposed modules. Code: https://github.com/lingli1724/PointVG-R.

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14.

arXiv (CS.LG) 2026-06-16 DOI: arXiv:2606.16034

Inference-Time Decision Calibration for Temporal Classification

Authors:

Arthur Chagas↗Arthur Buzelin↗Yan Aquino↗Pedro Bento↗Gisele L. Pappa↗Wagner Meira Jr↗Cristiano Arbex Valle↗

arXiv:2606.16034v1 Announce Type: new Abstract: Temporal classification errors are often treated as representation failures, but they can also arise from how available evidence is converted into decisions. This paper proposes a representation–calibration decomposition for temporal classification. We keep a trained native classifier frozen and separate two inference-time interventions: a conservative residual multi-scale branch that adds auxiliary logits to the native prediction, and a post-hoc branch-aware calibrator that recombines native and residual evidence at decision time. This design distinguishes missing temporal evidence from underused decision-level evidence without retraining the backbone. Across FI-2010, PTB-XL, UCI-HAR, MHEALTH, and HARTH, we find that gains are strongly regime-dependent. Residual multi-scale evidence is most useful in noisy or representation-limited settings, especially short-horizon FI-2010 and weaker recurrent backbones, while branch-aware calibration helps when native and auxiliary logits contain complementary evidence not fully exploited by the raw decision rule. Near-saturated settings show limited gains from either intervention. These results suggest that temporal classification should be understood not only as representation learning, but also as the problem of trusting, combining, and calibrating evidence from multiple views.

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15.

arXiv (CS.CV) 2026-06-15 DOI: arXiv:2605.25651

Hierarchical Consistency Learning for Test-time Adaptation in Camouflage Perception

Authors:

Mingfeng Zha↗Tianyu Li↗Guoqing Wang↗Yunqiang Pei↗Chaofan Qiao↗Jiening Zhang↗Yang Yang↗Heng Tao Shen↗

Camouflaged object detection (COD) aims to localize targets that exhibit minimal perceptual differences from backgrounds through physical attributes. Existing methods, constrained by the static train-then-freeze paradigm, suffer from domain rigidity and annotation dependency, limiting their adaptability to scene variations and unseen camouflage patterns. To overcome these, we propose the hierarchical consistency learning (HCL) framework, which integrates test-time adaptation for dynamic representation recalibration. Specifically, we design the hierarchical representation reconstruction (HRR) to alleviate feature entanglement by synergizing spatial reconstruction with dual-stream frequency-domain decomposition, enhancing robustness against appearance homogenization. The pixel and spectrum inference provide structural and contextual priors. We further introduce task affinity guidance (TAG) to propagate knowledge across branches via channel-wise affinity, aligning local discriminative cues and mitigating semantic drift. To ensure semantic invariance, we formulate the prototype consistency calibration (PCC), which aggregates region features into compact prototypes and establishes prototype-feature similarity. This imposes implicit and hierarchical constraints that bridge task and representation gaps. Extensive experiments across four camouflaged and four underwater object benchmarks, under three degradation settings, demonstrate that our method consistently outperforms state-of-the-art approaches, highlighting its robustness and generalization under distribution shifts.

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16.

arXiv (CS.AI) 2026-06-16 DOI: arXiv:2603.17353

Learning Permutation Distributions via Reflected Diffusion on Ranks

Authors:

Sizhuang He↗Yangtian Zhang↗Shiyang Zhang↗David van Dijk↗

arXiv:2603.17353v2 Announce Type: replace-cross Abstract: The finite symmetric group S_n provides a natural domain for permutations, yet learning probability distributions on S_n is challenging due to its factorially growing size and discrete, non-Euclidean structure. Recent permutation diffusion methods define forward noising via shuffle-based random walks (e.g., riffle shuffles) and learn reverse transitions with Plackett-Luce (PL) variants, but the resulting trajectories can be abrupt and increasingly hard to denoise as n grows. We propose Soft-Rank Diffusion, a discrete diffusion framework that replaces shuffle-based corruption with a structured soft-rank forward process: we lift permutations to a continuous latent representation of order by relaxing discrete ranks into soft ranks, yielding smoother and more tractable trajectories. For the reverse process, we introduce contextualized generalized Plackett-Luce (cGPL) denoisers that generalize prior PL-style parameterizations and improve expressivity for sequential decision structures. Experiments on sorting and combinatorial optimization benchmarks show that Soft-Rank Diffusion consistently outperforms prior diffusion baselines, with particularly strong gains in long-sequence and intrinsically sequential settings.

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17.

arXiv (CS.AI) 2026-06-12 DOI: arXiv:2606.12935

MARS: Margin-Adversarial Risk-controlled Stopping for Parallel LLM Test-time Scaling

Authors:

Wenbo Chen↗Puheng Li↗Mengyang Liu↗Weijie Su↗Tianpei Xie↗

arXiv:2606.12935v1 Announce Type: new Abstract: Parallel test-time scaling samples many reasoning traces and majority-votes their answers, improving LLM accuracy but requiring traces to run to completion, incurring substantial computational overhead. We observe that probing partial traces at intermediate checkpoints can extract current answers without disrupting generation, revealing an evolving aggregate vote. Based on this observation, we introduce MARS, a margin-adversarial stopping rule that estimates which active traces are likely to change their answers and stops once the leader remains safe under a conservative bound on future vote movement. The rule separates two sources of uncertainty. It learns the trace-level switch probabilities that determine how much of the current margin is likely to be retained, while handling the harder question of where switching traces land through an adversarial bound calibrated from warmup traces. With true switch probabilities, MARS guarantees with high probability that the early-stopped answer matches the full-budget vote. In practice, a five-feature logistic model closely matches oracle switching behavior. Across three reasoning models and three competition-math benchmarks, MARS saves 25-47% of self-consistency tokens and 14-29% on top of DeepConf Online, a strong confidence-weighted baseline that already filters and truncates weak traces, while matching the accuracy of the corresponding full-budget baselines.

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18.

arXiv (CS.CL) 2026-06-11 DOI: arXiv:2510.23508

M4FC: a Multimodal, Multilingual, Multicultural, Multitask Real-World Fact-Checking Dataset

Authors:

Jiahui Geng↗Jonathan Tonglet↗Iryna Gurevych↗

Existing real-world datasets for multimodal fact-checking have multiple limitations: they contain few instances, cover on only one or two languages, focus only on one task, or rely on external news article sets for sourcing true claims. To address these shortcomings, we introduce M4FC, a new real-world dataset comprising 4,982 images paired with 6,980 claims. The images, verified by professional fact-checkers from 22 organizations, represent a diverse range of cultural and geographic contexts. Each claim is available in one or two out of ten languages. M4FC spans six multimodal fact-checking tasks: visual claim extraction, claimant intent prediction, fake image detection, image contextualization, location verification, and verdict prediction. We provide baseline results for all tasks and analyze how combining intermediate tasks affects verdict prediction performance. We make our dataset and code publicly available.

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19.

arXiv (CS.LG) 2026-06-16 DOI: arXiv:2602.16793

Escaping the Cognitive Well: Efficient Competition Math with Off-the-Shelf Models

Authors:

Xingyu Dang↗Rohit Agarwal↗Rodrigo Porto↗Anirudh Goyal↗Liam H Fowl↗Sanjeev Arora↗

arXiv:2602.16793v2 Announce Type: replace Abstract: In the past year, custom and unreleased math reasoning models reached gold medal performance on the International Mathematical Olympiad (IMO). Similar performance was then reported using large-scale inference on publicly available models but at prohibitive costs (e.g., 3000 USD per problem). In this work, we present an inference pipeline that attains best-in-class performance on IMO-style math problems at an average inference cost orders of magnitude below competing methods while using only general-purpose off-the-shelf models. Our method relies on insights about grader failure in solver-grader pipelines, which we call the Cognitive Well (iterative refinement converging to a wrong solution that the solver as well as the pipeline's internal grader consider to be basically correct). Our pipeline addresses these failure modes through conjecture extraction, wherein candidate lemmas are isolated from generated solutions and independently verified alongside their negations in a fresh environment (context detachment). On IMO-ProofBench Advanced (PB-Adv), our pipeline achieves 67.1 percent performance using Gemini 3.0 Pro with an average cost per question of approximately 31 USD. At the time of evaluation, this represented the state-of-the-art on PB-Adv among both public and unreleased models, and more than doubles the success rate of the next best publicly accessible pipeline, all at a fraction of the cost.

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20.

arXiv (CS.CV) 2026-06-24 DOI: arXiv:2606.24726

SER: Learning to Ground Video Reasoning with Semantic Evidence Rewards

Authors:

Sheng Xia↗Zhengqin Lai↗Tianxiang Jiang↗Kanghui Tian↗Shoujun Zhou↗Bin Li↗Yi Wang↗

Video MLLMs often struggle with fine-grained spatio-temporal reasoning, sometimes generating correct answers based on irrelevant frames or objects. Although outputting spatio-temporal evidence during reasoning is a promising direction, existing RL frameworks typically rely on geometry-only (IoU) rewards, which can be sensitive to boundary perturbations and overlook semantic alignment. To address this, we propose Semantic Evidence Reward (SER), which reformulates spatio-temporal evidence grounding as a constrained verification task. Instead of computing pixel-level overlap, SER uses a referee VLM as a local checker to evaluate model-generated evidence claims across two dimensions: relevance and localization quality, combined with a temporal penalty. This design reduces the reliance on dense box annotations and enables training directly on standard video QA data. On the V-STAR benchmark, SER achieves 49.6% mLGM, improving by 3.0 points over the strong evidence-grounded baseline Open-o3-Video, demonstrating its potential in enhancing both answer accuracy and evidence grounding.

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21.

arXiv (CS.AI) 2026-06-18 DOI: arXiv:2505.12369

Fully Geometric Multi-Hop Reasoning on Knowledge Graphs with Transitive Relations

Authors:

Fernando Zhapa-Camacho↗Robert Hoehndorf↗

arXiv:2505.12369v2 Announce Type: replace Abstract: Multi-hop logical reasoning on knowledge graphs requires faithfully mapping the logical semantics to latent space. Current geometric embedding methods show to be useful on this task by mapping entities to geometric regions and logical operations to latent transformations. While a geometric embedding can provide a direct interpretability framework for query answering, current methods have only leveraged the geometric construction of entities, failing to map logical operations to pure geometric transformations and, instead, using neural components to learn these operations. On the other hand, purely neural-based methods outperform geometric methods, but they lack interpretability in the latent space. We introduce GeometrE, a geometric embedding method for multi-hop reasoning, that maps every logical operation to a purely geometric operation in the latent space. Additionally, we introduce a transitive loss function and show that, unlike existing methods, it can preserve the logical rule for all a,b,c: r(a,b) and r(b,c) -> r(a,c). Our experiments show that GeometrE outperforms current state-of-the-art geometric methods and remains competitive with existing neural-based methods on standard benchmark datasets.

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22.

bioRxiv (Bioinfo) 2026-06-15 DOI: HASH:f7fb1904dd55959661ed2bc73bb4c000

SMS: Symmetric Mediation Statistics for Powerful High-Dimensional Mediation Analysis

Authors:

Wang↗Yan↗H.-J↗Hu↗Y.-J↗

Background: Mediation analysis of high-dimensional features, particularly molecular-level omics features, provides important opportunities to uncover biological mechanisms underlying human health and disease. However, two central statistical challenges remain: testing the composite-null hypothesis and maintaining power when the exposure-mediator and mediator-outcome associations differ substantially in statistical significance. Existing methods typically rely on accurate estimation of the proportions of the three null types or on the maximum of the two association p-values, and may not always control the FDR well and may have limited power under imbalanced significance. Methods: We propose SMS, a new statistical framework based on symmetric mediation statistics. By exploiting symmetry, SMS calibrates the composite null distribution as a whole for FDR control. It also allows flexible combinations of the two association p-values, including the maximum, and then enables construction of an omnibus test. Moreover, it permits direct use of effect-size estimates, bypassing the need to compute p-values. Results: SMS controlled the FDR across a wide range of simulation scenarios while achieving a substantial sensitivity gain, often around 20 percentage points, over existing methods including HDMT, DACT, and DEI-B. Applications to a metabolomics dataset and a DNA methylation dataset further corroborated these findings. Notably, SMS discovered five plausible mediators in the metabolomics dataset that were missed by all existing methods considered.

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23.

arXiv (CS.CL) 2026-06-16 DOI: arXiv:2605.21850

ACC: Compiling Agent Trajectories for Long-Context Training

Authors:

Qisheng Su↗Zhen Fang↗Shiting Huang↗Yu Zeng↗Yiming Zhao↗Kou Shi↗Ziao Zhang↗Lin Chen↗Zehui Chen↗Lijun Wu↗Feng Zhao↗

Recent development of agents has renewed demand for long-context reasoning capacity of LLMs. However, training LLMs for this capacity requires costly long-document curation or heuristic context synthesis. We observe that agents produce massive trajectories when solving problems, invoking tools and receiving environment observations across many turns. The evidence needed to answer the original question is thus scattered throughout these turns, requiring integration of distant context segments. Nevertheless, standard agent SFT masks tool responses and only trains turn-level tool selection, creating a supervision blind spot where these scattered signals go unused. We propose Agent Context Compilation (ACC), which converts trajectories from search, software engineering, and database querying agents into long-context QA pairs that combine the original question with tool responses and environment observations gathered across multiple turns, training the model to answer directly without tool use. This makes the dependencies between the question and the evidence explicit, enabling direct supervision of long-context reasoning over distant segments without additional annotation. ACC is a simple but effective approach that can be combined with any existing long-context extension or training method, providing scalable supervised fine-tuning data. We validate ACC on long-range dependency modeling tasks through MRCR and GraphWalks, challenging benchmarks requiring cross-turn coreference resolution and graph traversal over extended contexts. Training Qwen3-30B-A3B with ACC achieves 68.3 on MRCR (+18.1) and 77.5 on GraphWalks (+7.6), results comparable to Qwen3-235B-A22B, while preserving general capabilities on GPQA, MMLU-Pro, AIME, and IFEval. Further mechanism analysis reveals that the ACC-trained model exhibits task-adaptive attention restructuring and expert specialization.

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24.

medRxiv (Medicine) 2026-06-22 DOI: HASH:d9c89869084c7de63dc1a4203e0b7811

Vaccine introductions in the WHO African Region, 2023-26: a country-level ecological analysis by Gavi eligibility and conflict-affected status

Authors:

Amani↗Mitula↗Bekele↗A. T↗Danwang↗Ngossaki↗H. D↗Masembe↗Y. V↗Agbenu↗A. E↗Achille↗…

Background. The Immunization Agenda 2030 (IA2030) tracks new and underused vaccine introduction as an access metric, and its mid-term review calls for stronger country ownership, prioritisation, data use and tailored support in conflict-affected and resource-constrained settings; however, national launch status does not measure recurrent financing, implementation, safety or equity. We examined how recent vaccine-introduction activity was distributed across the WHO African Region. Methods. We conducted a descriptive country-level ecological analysis of all 47 Member States from January 2023 to June 2026. The country was the unit of analysis and contributed one cumulative, unweighted count of nationally endorsed vaccine-introduction and programme-change events. Counts were linked to Gavi eligibility, World Bank FY26 conflict-affected status, broader fragile and conflict-affected situation status in sensitivity analysis, and concurrent system-performance indicators, and modelled with Poisson regression using HC1 robust standard errors. Two Expanded Programme on Immunization (EPI) manager survey waves were summarised at country level. Reporting followed STROBE and RECORD. Results. Seventy-two events were recorded across 38 of 47 Member States: 48 new-antigen introductions, 20 dose or schedule expansions and four combination-vaccine introductions; malaria vaccines accounted for 21. Gavi-eligible conflict-affected countries averaged 2.50 events per country versus 1.27 in both comparison groups. Gavi-eligible conflict-affected status was associated with a higher count (incidence rate ratio [IRR] 1.97, 95% confidence interval [CI] 1.38-2.81; p

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25.

arXiv (CS.LG) 2026-06-18 DOI: arXiv:2606.18286

CODEBLOCK: Learning to Supervise Code at the Right Granularity

Authors:

Zhijie Deng↗Ling Li↗Jinlong Pang↗Kaiqin Hu↗Qi Xuan↗Zhaowei Zhu↗Jiaheng Wei↗

arXiv:2606.18286v1 Announce Type: new Abstract: Supervised fine-tuning of code LLMs typically applies uniform cross-entropy loss to all response tokens, implicitly assuming that every token provides equally useful learning signal. Recent token-level selection methods challenge this assumption in natural-language SFT by supervising only high-value tokens. However, directly transferring token-level masking to code can break syntactically and semantically coherent program units, because code depends on structural completeness and definition-use relations. We therefore propose CodeBlock, a structure-aware sparse supervision framework that selects structure-complete code evidence rather than isolated tokens. CodeBlock first selects high-quality instruction-response pairs, then partitions code responses into syntactically coherent coding items, estimates their utility by aggregating generalized cross-entropy over core logic tokens, and reranks them with data-flow reach and bridge signals to prioritize blocks that propagate or connect important program dependencies. During training, the full response remains available as context, while loss is applied only to selected code items and informative natural-language tokens. Experiments on six code-generation benchmarks show that CodeBlock achieves stronger average pass@1 than full-token SFT and competitive selection baselines, while using only 1.9% of supervised response tokens.

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