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02.
arXiv (CS.AI) 2026-06-16

InvDesMobility: a reliability-gated first-principles feedback framework for closed-loop materials discovery

作者:
Wen-Kao Li↗Ze-Feng Gao↗Peng-Jie Guo↗Wei Ji↗Zhong-Yi Lu↗

arXiv:2606.16133v1 Announce Type: cross Abstract: Inverse materials design starts from target functionality and searches for structures that can realize it. Its value in closed-loop discovery depends not only on prediction performance, but also on whether expensive first-principles results are independently validated, provenance-recorded, and admitted as feedback only when evidence is sufficient. This is especially important for composite properties such as carrier mobility, where a final scalar value hides intermediate quantities, fit quality, convergence history, and workflow assumptions. Here we present InvDesMobility, a reliability-gated first-principles feedback framework that integrates multi-agent automated DFT, evidence stratification, generative structure proposal, acquisition ranking, and auditable release. Using 516 2DMatPedia-derived candidates, the workflow produced 280 QC-passed materials and 573 retained carrier-direction seed channels after channel-level reliability gating. These records were split into two feedback objects: relaxed structures updated the generative model, while retained mobility channels trained the acquisition model and set validation priority. Over multiple iterations, InvDesMobility screened 2.4 x 10^6 structures, submitted 102 candidates for DFT validation, and retained 86 reliability-gated generated channels across 41 formulas. Overall, the main contribution is not a fixed list of high-mobility materials, but a transferable feedback contract that makes closed-loop inverse design both useful and auditable when learning from expensive calculated properties. All source data, retained feedback records, and workflows are available at https://github.com/DreamLufei/invDesMobility, with an accompanying evidence website at https://dreamlufei.github.io/invDesMobility/.

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03.
arXiv (CS.LG) 2026-06-16

Exact Federated Continual Unlearning for Ridge Heads on Frozen Foundation Models

作者:
Yijun Quan↗Wentai Wu↗Giovanni Montana↗

arXiv:2603.12977v3 Announce Type: replace Abstract: Foundation models are commonly deployed as frozen feature extractors with a small trainable head to adapt to private, user-generated data in federated settings. The ``right to be forgotten'' requires removing the influence of specific samples or users from the trained model on demand. Existing federated unlearning methods target general deep models and rely on approximate reconstruction or selective retraining, making exactness costly or elusive. We study this problem in a practically relevant but under-explored regime: a frozen foundation model with a ridge-regression head. The exact optimum depends on the data only through two additive sufficient statistics, which we turn into a communication protocol supporting an arbitrary stream of add and delete requests via fixed-size messages. The server maintains a head that is, in exact arithmetic, pointwise identical to centralized retraining after every request. We provide deterministic retrain-equivalence guarantees, order and partition invariance, two server-side variants, and a Bayesian certificate of zero KL divergence. Experiments on four benchmarks confirm the guarantees: both variants match centralized ridge retraining to within $10^{-9}$ relative Frobenius error and complete each request at orders-of-magnitude lower cost than federated retraining baselines.

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04.
arXiv (quant-ph) 2026-06-19

Quantum Batteries as Work Sources for Phase-Locked Parametric Amplification

作者:
Borhan Ahmadi↗

arXiv:2606.20306v1 Announce Type: new Abstract: Quantum batteries have been proposed as locally precharged work sources for superconducting quantum technologies, suggesting a route to reduce continuously supplied microwave drives. Here we ask whether the pump tone of a quantum-limited parametric amplifier can be replaced, or strongly duty-cycled, by a finite bosonic quantum battery. Quantizing the pump of a nondegenerate parametric amplifier exposes a resource distinction hidden in the classical description: stored pump energy can generate signal-idler photons, but pump phase coherence is required to generate a phase-locked amplifier field. In a closed trilinear model, coherent and phase-randomized coherent pumps with the same photon-number distribution produce comparable pair numbers, yet only the coherent pump produces anomalous two-mode coherence and an EPR-squeezed interference dip. Including leakage, we collect the emitted fields into cascaded temporal modes. At matched collector bandwidth, the coherent pump gives \(I_{\min}^{(f)}=0.553\), whereas the phase-randomized pump gives \(I_{\min}^{(f)}=1.94\) at nearly identical collected energy. Weak amplitude squeezing slightly improves the dip by reducing finite-pump number fluctuations while preserving the coherent displacement. Thus battery-powered parametric amplification requires phase-coherent stored energy, possibly assisted by number-noise reduction, rather than stored energy alone.

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05.
arXiv (CS.LG) 2026-06-18

Mixed-Precision Communication-Avoiding SGD for Generalized Linear Models on GPUs

作者:
Aditya Devarakonda↗Irene Sim\'o Mu\~noz↗Giulia Guidi↗

arXiv:2606.18463v1 Announce Type: cross Abstract: Distributed stochastic gradient descent (SGD) is limited by communication rather than computation, since each iteration requires an AllReduce across processes. Communication-avoiding SGD (CA-SGD) amortizes communication over $s$ iterations by replacing $s$ consecutive AllReduces with a single AllReduce of an $sb\times sb$ Gram matrix, trading more computation and bandwidth for fewer synchronization points. Modern GPUs with matrix hardware and reduced-precision formats offset this by accelerating the Gram GEMM and shrinking BF16 traffic. We study mixed-precision CA-SGD for generalized linear models on NVIDIA GPUs. Our finite-precision analysis decomposes the local rounding error of one CA-SGD outer iteration into nine independent precision choices, depending on the hardware only through its low-precision unit roundoffs, so the resulting recipes transfer in principle across GPU generations. The recipe stores the input matrix and margin vector in low precision, computes the Gram matrix from low-precision inputs with high-precision accumulation, communicates it in high precision, and performs the inner recurrence and weight updates in high precision. On NERSC Perlmutter A100 GPUs, mixed-precision CA-SGD matches FP32 SGD loss within $0.5\%$ on logistic, linear, and Poisson problems and reaches $5.1$–$6.8\times$ speedup over FP32 SGD on epsilon, SUSY, HIGGS, synth, and Poisson-synth. Our software is available at https://doi.org/10.5281/zenodo.20448273

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06.
arXiv (quant-ph) 2026-06-15

Calibrated Helstrom geometry on the Bloch ball via Connes spectral distance

作者:
Kaushlendra Kumar↗

arXiv:2606.13824v1 Announce Type: new Abstract: We show that the equal-prior Helstrom trace-distance geometry of qubit states is recovered from Connes spectral distance in a finite scalar-qubit-scalar model. The two scalar reference sectors couple isotropically to the qubit block through identity Dirac links, so that the full Bloch ball, including mixed states, inherits its standard chordal trace-distance geometry from the finite spectral metric. The scalar-sector distances serve a distinct calibration role: they determine the individual link lengths, satisfy a Pythagorean consistency relation, and reconstruct the middle-sector scale.

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07.
arXiv (CS.AI) 2026-06-12

Divination by Prompt: LLM-Mediated Xuanxue on Chinese Social Media

作者:
Chuang Li↗Lixuan Wang↗Yuqi Chen↗Ze Hong↗

arXiv:2606.12418v1 Announce Type: cross Abstract: The rapid proliferation of large language models (LLMs) has produced a striking cultural practice: using conversational AI for divination. This paper offers one of the first systematic studies of LLM-mediated divination in the context of Xuanxue, an internet-native umbrella term for mystical and spiritual practices on Chinese social media. Using a mixed-methods design, we analyze 23000+ posts and comments from Xiaohongshu and conduct 32 semi-structured interviews with users and professional diviners. Users primarily consult LLMs about pragmatic concerns - romantic relationships, careers, exams, and in-game gacha draws - via two intersecting pathways: trend-driven curiosity enabled by viral visibility and zero-cost access, and event-driven anxiety under conditions of uncertainty. A defining feature is collaborative prompt refinement, which turns users into active prompt engineers. Among commenters expressing a clear stance, perceived efficacy skews positive, with "accuracy" often justified through biographical fit and retrospective confirmation, consistent with Barnum and confirmation bias. Users also develop verification practices such as repeated trials and cross-model comparison. Professional diviners, by contrast, portray LLMs as lacking the "spiritual power" required for genuine divination, reflecting both ontological commitments and economic boundary-work. We also show how participants navigate tensions between scientific and metaphysical frames when interpreting AI-generated readings. Situating these findings in anthropological and cognitive-evolutionary theories of divination, we argue that LLM divination preserves core functions of traditional practice while introducing scalability, repeatability, and prompt-driven co-production that reshape how divinatory authority is constructed and evaluated.

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08.
arXiv (CS.CL) 2026-06-25

Constraint Tax in Open-Weight LLMs: An Empirical Study of Tool Calling Suppression Under Structured Output Constraints

作者:
Fangzheng Li↗Aimin Zhang↗Chen Lv↗

Tool Calling and Structured Output are two core capabilities of modern Agent systems, yet their interaction under joint deployment conditions remains insufficiently understood. This paper reports a reproducible phenomenon observed in a production Agent system: when Tool Calling and JSON Schema constraints are simultaneously enabled, multiple open-weight models cease invoking tools despite maintaining high schema compliance. We refer to this behavior as Tool Suppression. Through controlled experiments across multiple model families and deployment settings, we consistently reproduce Tool Suppression under joint constraints, while tool execution and schema compliance remain functional when evaluated independently. Further analysis reveals that JSON Schema constraints are compiled into grammar-based token masks, causing tool-call tokens to become unreachable during decoding. This provides an implementation-level explanation for the observed behavior. To interpret the phenomenon, we formulate the Constraint Priority Inversion (CPI) hypothesis, which suggests that schema satisfaction may dominate action-selection behavior under multiple simultaneous constraints. We present CPI as a behavioral hypothesis consistent with the observed evidence rather than a verified internal mechanism. To mitigate the problem, we propose Transparent Two-Pass Execution, an inference-time strategy that decouples tool execution from schema-constrained response generation. Experimental results show that this approach restores tool invocation while preserving structured output guarantees without requiring model retraining. These findings suggest that evaluating tool use and structured output separately may overlook important reliability issues in production Agent systems. Code, data, and docs will be released at https://github.com/Fzsama/Constrain-Tax-26-06.git.

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09.
Nature Biotechnology 2026-06-23

Efficient generation of epitope-targeted antibodies with Germinal

作者:
Luis S. Mille-Fragoso↗

Obtaining antibodies to specific protein targets is a widely important yet experimentally laborious process. Meanwhile, computational methods for antibody design have been limited by low success rates that require resource-intensive screening. Here we introduce Germinal, a broadly enabling generative pipeline that designs antibodies against specific epitopes with nanomolar binding affinities while requiring only low-n experimental testing. Our method co-optimizes antibody structure and sequence by integrating a structure predictor with an antibody-specific protein language model to perform de novo design of functional complementarity-determining regions onto a user-specified structural framework. When tested against four diverse protein targets, Germinal designed functional antibodies across all targets and binder formats, testing only 43–101 designs for each antigen. Validated designs also exhibited robust expression in mammalian cells and high sequence and structural novelty. We provide open-source code and full computational and experimental protocols to facilitate wide adoption. Germinal achieves epitope-targeted, de novo complementarity-determining region design with high experimental success rates.

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10.
arXiv (CS.CV) 2026-06-16

Look Again Before You Abstain:Budgeted Conformal Evidence Acquisition for Reliable Vision-Language Model

作者:
Jian Xu↗Delu Zeng↗John Paisley↗Qibin Zhao↗

Large vision-language models (LVLMs) hallucinate: they assert visual details that the image does not support. A principled remedy is selective prediction with a distribution-free guarantee-verify each claim and abstain when the claim is not grounded, so that the hallucination rate among asserted claims is provably bounded. We show, however, that this guarantee is bought at a brutal price: to keep the hallucination rate below $5\%$ on a balanced object-existence benchmark, a state-of-the-art conformal filter must abstain on more than $80\%$ of claims. We argue that abstention is wasteful when more visual evidence is cheaply available, and introduce Budgeted Conformal Evidence Acquisition (BCEA), which replaces the binary answer/abstain decision with a three-way choice: answer, abstain, or acquire additional visual evidence by re-examining the image (zooming, cropping, or applying a claim-specific intervention) under a bounded compute budget. We make two observations. First, acquisition that is plugged naively into a calibrated filter breaks the statistical guarantee – realized risk overshoots the target by up to $17$ points – because the acquisition step destroys the exchangeability that conformal calibration relies on. Second, folding the entire acquisition policy into the score function and re-calibrating on post-acquisition scores restores the finite-sample guarantee while still recovering coverage. BCEA further uses structured, claim-type-specific interventions. Across the POPE benchmark and COCO-constructed existence and spatial-relation claims, on four open VLMs, BCEA controls the hallucination rate at the target level and consistently improves coverage over a guaranteed-abstention baseline.

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11.
arXiv (CS.CL) 2026-06-16

Mechanistic Analysis of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning

作者:
Gustav Olaf Yunus Laitinen-Fredriksson Lundstrom-Imanov↗

Sequential fine-tuning of Large Language Models (LLMs) adaptation to target tasks often triggers catastrophic forgetting, where the acquisition of novel target skills degrades ancestral capabilities. This paper presents a systematic comparative study of catastrophic forgetting across twenty premier models representing the state-of-the-art in mid-2026. We categorize our investigation into two primary research lines: (i) a behavioral and semantic output drift analysis of ten leading closed-source models (including Claude Fable 5, GPT-5.5 High, and Gemini 3.5 Flash), and (ii) a deep mechanistic interpretation of ten prominent open-weight architectures (such as DeepSeek-V4-Pro, Llama 4 Maverick, and Qwen 3.6-27B). Through weight-space trajectory tracking, Centered Kernel Alignment (CKA), and routing gate drift calculations in Mixture-of-Experts (MoE) layers, we localize the neural circuits highly susceptible to parameter overwriting. Our findings indicate that early-layer attention heads exhibit systemic entropic dispersion, while mid-to-deep feed-forward networks (or sparse expert blocks) suffer localized representation collapse. Informed by these insights, we introduce Low-Rank Circuit Projection (LRCP), a subspace-regularized training intervention. Empirical evaluations show that LRCP successfully mitigates up to 94.2% of ancestral capabilities in open-weight configurations and matches the adaptation velocity of standard PEFT baselines.

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12.
arXiv (quant-ph) 2026-06-16

Benchmarking Quantum Computers via Protocols, Comparing IBM's Heron vs IBM's Eagle

作者:
Nitay Mayo↗Tal Mor↗Yossi Weinstein↗

arXiv:2603.04377v3 Announce Type: replace Abstract: As quantum computing hardware rapidly advances, objectively evaluating the capabilities and error rates of new processors remains a critical challenge for the field. A clear and realistic understanding of current quantum performance is essential for guiding research priorities and driving meaningful progress. In this work, we apply and extend a protocol-based benchmarking methodology (Meirom, Mor, Weinstein Arxiv 2505.12441) that utilizes well-defined \underline{quantumness} thresholds. By evaluating performance at protocol level rather than the gate level, this approach provides a transparent and intuitive assessment of whether specific quantum processors, or isolated sub-chips within them, can demonstrate a practical quantum advantage. To illustrate the utility of this method, we compare two generations of IBM quantum computers: the older Eagle architecture and the newer Heron architecture. Our findings reveal the genuine operational strengths and limitations of these devices, demonstrating substantial performance improvements in the newer Heron generation. This work was made possible by IBM Quantum policies that enable independent and objective assessment of its quantum computers and sub-chips. We strongly encourage other companies to emulate the independent qubit availability and the fair pricing that allow researchers to perform such assessments.

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13.
arXiv (CS.CV) 2026-06-16

HorusEye: Language as Dynamic Attention for Emergency Visual Analysis

作者:
Armel Yara↗

We introduce HorusEye, Language as Dynamic Attention for Emergency Visual Analysis. Our investigation followed five stages. The first one is benchmarking RefCOCO-Degraded, a dataset of 15,244 images (3,811 base images x 4 conditions: Clean, Fog, Smoke and Thermal) with systematic visual degradation. Through four research questions, we evaluate multiple VLMs (Gemini, Qwen2-VL, BLIP-2, LLaVA, Kosmos-2) across visual grounding the second stage, language feedback recovery the third one, health VQA tasks the fourth, and hallucination analysis the final stage. Our key finding is that language feedback effectiveness is model-dependent: Gemini achieves +47.3% improvement in thermal conditions through iterative language feedback, while Qwen2-VL shows -5.1% degradation under the same protocol. We also identify the 'Thermal Paradox' where cropping strategies that improve RGB performance catastrophically fail in thermal imagery. Furthermore, BLIP-2 uniquely hallucinates more under degradation, making it unsuitable for emergency deployment

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14.
arXiv (quant-ph) 2026-06-25

Polymer quantum mechanics on compact configuration spaces

作者:
Maxwell R. Siebersma↗Basie Seibert↗Samuel Shuman↗David A. Craig↗

arXiv:2606.06019v2 Announce Type: replace Abstract: "Polymer quantum mechanics" is the name given to a quantization scheme inspired by loop quantum gravity in which the configuration space of the theory is chosen to have a discrete topology. Polymer quantization yields a representation of the canonical commutation relations that is genuinely distinct from the conventional "Schrödinger" representation. In this paper, we summarize the main features of polymer quantum mechanics and investigate in detail the polymer quantization of systems with configuration spaces that are classically compact. We show explicitly how using the standard construction of polymer states leads to a Hilbert space of states defined on a finite graph of points. By way of example, we find the exact energy eigenvalues and eigenfunctions for a particle on a ring and a particle in a box defined on such lattices, and discuss similarities and differences from standard Schrödinger quantum mechanics. We also explore the continuum limit of states in these systems, and demonstrate in detail how the exact eigenfunctions in the position representation approach their continuum counterparts.

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15.
arXiv (quant-ph) 2026-06-24

M{\o}lmer-S{\o}rensen gates in trapped-ions chains in the presence of correlated noise

作者:
D. V. Donchenko↗E. A. Anikin↗O. Lakhmanskaya↗K. Lakhmanskiy↗

arXiv:2606.23951v1 Announce Type: new Abstract: We analyze the impact of correlated laser frequency noise on M{\o}lmer-S{\o}rensen gates in qubit registers based on trapped-ion chains. Using perturbation theory, we calculate gate fidelities in the presence of noise with arbitrary power spectral density for different chain lengths and ion positions in the chain. With our approach, we account for simultaneous excitation of multiple phonon modes during gate operation. We find out that the impact of medium-frequency laser noise depends considerably on the positions of the ions in the chain. In contrast, low-frequency noise has similar effect for different chain lengths and ion positions.

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16.
medRxiv (Medicine) 2026-06-17

Multi-strain Probiotics Alter Gut Microbiota and Estrobolome Pathways in Primary Dysmenorrhea

作者:
Kiraman↗S. K↗Zakaria↗I. A↗Loh↗S. Y. E↗Norfuad↗F. A↗Abdul Ghani↗N. A↗Ahmad↗H. F↗…

Background: Exact cause of primary dysmenorrhoea is unknown but recent evidence uncovers a potential link between gut dysbiosis and benign gynaecological disorder via disruption of estrobolome. Methods: A randomized controlled trial to investigate the effects of multi-strain oral probiotics on primary dysmenorrhoea has been conducted. This is a secondary analysis comparing the stool microbiome in women with primary dysmenorrhoea and those without (control), and the effects of treatment with probiotics versus placebo. Results: Although microbial richness and evenness were comparable between groups (alpha diversity, p > 0.05), gut microbial community composition differed significantly (Bray Curtis PERMANOVA, p = 0.015), characterised by reduced Bifidobacterium adolescentis and Blautia and enrichment of Faecalibacterium in dysmenorrhoea, alongside condition-specific core taxa. Post-intervention analysis revealed significant shifts in microbial community structure between pre- and post-treatment groups (PERMANOVA, F = 2.11, p = 0.005), with probiotic supplementation inducing more consistent and directed microbiome changes than placebo, without altering alpha diversity (p > 0.05). Functional prediction showed no significant difference in overall beta glucuronidase pathway abundance (p > 0.05); however, dysmenorrhoea was associated with higher abundance of beta glucuronidase producing taxa (MaAsLin2, q < 0.05) that were differentially modulated by probiotic treatment. Conclusion: This discovery provides evidence on the microbial disruption in primary dysmenorrhoea as well as the benefit of probiotics to modulate the intestinal microbiota to improve the condition.

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17.
arXiv (quant-ph) 2026-06-16

VQE as Initial State Preparation for QPE on Heisenberg Spin-Glass Hamiltonians

作者:
Elijah Pelofske↗Stephan Eidenbenz↗

arXiv:2606.15061v1 Announce Type: new Abstract: Quantum Phase Estimation (QPE) is the quantum algorithmic workhorse for computing ground state energies of quantum Hamiltonians with quantum computers. Ground state energy calculation of physical systems is perhaps the most promising use case for quantum computing in terms of scientific and commercial value with a plausible path to outperformance of classical alternatives. This path, however, hinges on the availability of initial states for QPE with significant overlap with the true ground state. Using extensive (classical) numerical computations, we study whether the NISQ-era algorithm VQE (Variational Quantum Eigensolver) could be used to efficiently prepare high-overlap states of disordered fully-connected anisotropic Heisenberg spin glass quantum Hamiltonians with up to $15$ qubits. We find that (i) – consistent with widely held, but rarely numerically illustrated beliefs – VQE is generally unable to efficiently converge to the ground state for our Hamiltonians, which is a well-known issue with VQE due to a variety of factors including vanishing gradients and local minima; (ii) low energy states do not necessarily have large ground-state overlap, but there is typically a correlation between the two measures; (iii) adding more than three layers to the VQE ansatz neither improves overlap nor the energies found; and (iv) the best-found overlap scaling as a function of the Hamiltonian system size is not strongly exponentially decreasing, suggesting potential for VQE to be a heuristic state preparation algorithm for QPE.

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18.
arXiv (CS.CL) 2026-06-24

CALIBER: Calibrating Confidence Before and After Reasoning in Language Models

作者:
Conor Finlay↗Joshua Kurien↗Saurabh Dash↗Marzieh Fadaee↗Beyza Ermis↗

Reasoning language models are increasingly asked not only to answer difficult questions, but also to estimate their likelihood of success. Existing methods typically elicit confidence only once: either before thinking or after answering. We argue that confidence in reasoning models is state-dependent: before thinking, confidence should estimate the chance of the model correctly solving the prompt, while after thinking it should predict whether the realized answer is likely to be correct. This distinction determines the appropriate supervision target: prompt-level success should supervise confidence estimates made after seeing the prompt, while individual answer-level correctness should supervise confidence estimates made after answering. We introduce CALIBER (Calibration Before and After Reasoning), which elicits both estimates and supervises each with the target matched to its information state. Under this unified protocol, CALIBER reduces Expected Calibration Error (ECE) by 52.5% over the strongest single-confidence baseline on BigMathDigits for the 7B model, while achieving the best Brier score and AUROC, and remains within 2.1 points of the best accuracy. Further, on a larger 30B model, CALIBER achieves the best ECE on BigMathDigits while remaining competitive in Brier score and AUROC. Out of distribution, it achieves the best ECE and Brier score on GPQA and TriviaQA, and remains competitive on SimpleQA. Ablations further show that this position-target alignment is most beneficial under distribution shift where it consistently reduces calibration error across all out-of-distribution benchmarks.

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19.
arXiv (CS.AI) 2026-06-12

Evoflux: Inference-Time Evolution of Executable Tool Workflows for Compact Agents

作者:
Kushal Raj Bhandari↗Ling Yue↗Ching-Yun Ko↗Dhaval Patel↗Shaowu Pan↗Pin-Yu Chen↗Jianxi Gao↗

arXiv:2606.12674v1 Announce Type: new Abstract: Compact language models (LMs) reduce cost, latency, and deployment risk for tool agents. Yet MCP-style tool use requires more than isolated function calling: an agent must discover tools from live catalogs, satisfy schemas, preserve dependencies across intermediate outputs, and ground final responses in executed evidence. Small planners often generate plausible workflow graphs that fail under tool resolution, parameter validation, dependency tracking, or execution. We argue that this failure mode is poorly handled by small-corpus distillation. A few hundred teacher traces can teach workflow format, but rarely cover the recovery behavior needed to repair failed plans over changing tool catalogs. We introduce Evoflux, an inference-time evolutionary search method that treats compact tool use as the repair of executable tool workflows. It evolves typed workflow graphs through structured edits, execution feedback, adaptive intensity, meta-guided redesign, and diversity pruning. On held-out MCP-Bench tasks spanning live MCP servers and 250 tools, Evoflux raises execution feasibility from roughly 3% to 17-24% across small planners. In contrast, SFT and SFT+DPO on the same search-mined data match, underperform, or collapse below zero-shot performance; ReAct reaches higher peaks, but with higher variance and token cost. These results show that execution-grounded search is more reliable under scarce teacher-trace budgets.

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21.
arXiv (CS.LG) 2026-06-12

Rubric-Guided Self-Distillation: Post-Training Without Rubric Verifiers

作者:
MohammadHossein Rezaei↗Anas Mahmoud↗Zihao Wang↗Utkarsh Tyagi↗Advait Gosai↗Razvan-Gabriel Dumitru↗Aakash Sabharwal↗Bing Liu↗Yunzhong He↗

arXiv:2606.12507v1 Announce Type: new Abstract: Rubrics have emerged as an alternative to RLVR in open-ended domains where a single ground-truth final answer is not available. Existing rubric-based training methods rely on an LLM verifier that scores each rollout against rubrics. This introduces substantial training-time overhead, exposes optimization to verifier-specific biases, and reduces rubric feedback to a sparse end-of-trajectory signal. We propose Rubric-Guided Self-Distillation (RGSD), a verifier-free training method in which the base policy, conditioned on the rubric, serves as the teacher for the unconditioned student. RGSD distills the rubric-conditioned teacher distribution into the student token-by-token, replacing sparse trajectory-level rewards with dense per-token learning signals and removing the LLM judge from the training loop entirely. Across Qwen-2.5 (3B, 7B) and Qwen3-Thinking (4B, 8B) models on medical and science domains, RGSD achieves rubric satisfaction comparable to judge-based GRPO while using one on-policy rollout per prompt and no training-time verifier calls. Ablations show that raw rubrics provide a stronger teacher enrichment signal than self-generated reference responses, while a stronger GRPO judge can outperform RGSD in some settings, positioning RGSD as a complementary verifier-free alternative when verifier cost or reliability is the bottleneck.

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22.
arXiv (CS.AI) 2026-06-25

Decoupling Reconnaissance and Exploitation: Measuring the Capability Boundaries of LLM-Based Web Penetration Testing

作者:
Liwei Yu↗Shuo Li↗Ming Zhou↗Ge Chu↗Yan Guo↗

arXiv:2606.25332v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown promise for automated penetration testing, yet existing end-to-end black-box evaluations are highly susceptible to error cascading: failures in early reconnaissance can mask an agent's actual ability to exploit vulnerabilities. To more accurately characterize these capabilities, we propose a two-stage decoupled evaluation framework that separates exploit execution from reconnaissance. Using ground-truth injection and knowledge-driven ablation across 70 high-fidelity web vulnerability testbeds, our framework isolates exploitation performance from reconnaissance noise. We empirically evaluate five open-source penetration-testing agents, covering multiagent, monolithic, and graph-driven architectures, on a strictly aligned subset of 50 representative vulnerabilities. The results reveal a substantial capability gap. With accurate vulnerability context, agents achieve a functional success rate of up to 90.0%, whereas autonomous reconnaissance, measured by targeted vulnerability recall, plateaus at approximately 50.0%, primarily due to failures in parsing unstructured telemetry. Cross-architectural analysis further reveals distinct capability niches: multi-agent isolation is more effective for long-sequence interactions such as de-serialization, while monolithic and graph-driven designs perform better on short-chain injections and cross-session access-control vulnerabilities, respectively. This decoupled evaluation work provides a fine-grained benchmarking protocol and an empirical basis for designing next-generation automated offensive security agents.

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23.
arXiv (quant-ph) 2026-06-17

Full-state information-disturbance tradeoff for direction estimation with antiparallel spin-coherent pairs

作者:
Massimiliano F. Sacchi↗

arXiv:2606.18040v1 Announce Type: new Abstract: We determine the optimal information–disturbance tradeoff for estimating an unknown spatial direction encoded in two antiparallel spins. Rotational covariance reduces the optimization over all instruments to a finite-dimensional Choi problem: a positive seed operator obeys one trace constraint for each irreducible sector of the input representation, while both the directional score and the operation fidelity are linear functionals of this seed. For two antiparallel spin-$1/2$ particles, whose physical representation decomposes as $0\oplus1$, we derive the two-multiplier dual problem and characterize the optimal instrument from the kernel vectors of the dual slack operator. The optimal operation is a covariant filter with scalar–vector coherence and is generally not a convex interpolation between the identity channel and a measure-and-reprepare strategy. At maximum information we recover the Gisin–Popescu score, but the least disturbing output state is optimized independently, giving a smaller disturbance than both the parallel-spin benchmark and antiparallel measure-and-reprepare. We also formulate the parallel benchmark and, as a central extension of the method, treat antiparallel spin-coherent states of arbitrary spin $j$. In this case the signal coherently occupies all sectors $\ell=0,\ldots,2j$ of $j\otimes j$, the endpoint information is governed by nearest-neighbor sector coherences, and the endpoint disturbance is obtained from an explicit finite block-diagonal eigenvalue problem.

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24.
arXiv (CS.LG) 2026-06-25

Do Prompt-Elicited Trajectories Reflect Training-Time Reward Hacking? A Systematic Study on Monitoring Trainig-Time Reward Hacking in Code Generation

作者:
Lichen Li↗Hengguang Zhou↗Yijun Liang↗Tianyi Zhou↗Cho-Jui Hsieh↗

arXiv:2604.23488v2 Announce Type: replace Abstract: Reward hacking in code generation, where models exploit evaluation loopholes to obtain high reward without correctly solving the intended task, poses a critical challenge for Reinforcement Learning (RL) and the deployment of reasoning models. Existing studies often rely on explicitly prompted hacking trajectories, but it remains unclear whether monitors trained on such data can detect reward hacks that arise without direct hacking instructions during RL training. In this work, we introduce Trace-and-Amplify, a framework for scalable curation of reward-hacking trajectories that arise during RL training without explicit hacking instructions. The framework uses unit-test tracers to identify hacking solutions when they occur and retains such trajectories for monitor training and evaluation. Through controlled comparisons between monitors trained on prompt-elicited hacking trajectories and training-time reward-hacking trajectories collected by Trace-and-Amplify, we find that (1) prompt-elicited-data-trained monitors often fail to generalize to trajectories curated by our framework, and (2) monitors trained on our Trace-and-Amplify trajectories demonstrate stronger generalizability to unseen hacking types. Our results indicate that prompted reward hacking data may not fully reflect training-time reward-hacking behaviors, and that relying solely on these data can lead to misleading conclusions. Codebase is available at https://github.com/LichenLillc/CoTMonitoring.git

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25.
arXiv (CS.CV) 2026-06-19

SAM3 Self-Distillation for Fine-Grained GOOSE 2D Semantic Segmentation

作者:
Xuesong Wang↗

We describe our 4th-place entry to the ICRA 2026 GOOSE 2D Fine-Grained Semantic Segmentation Challenge, which reached a composite mean Intersection-over-Union (mIoU) of 69.73% on the official 1,815-image test set. Our model adapts the image encoder of a recent visual foundation model, Segment Anything Model 3 (SAM3), with a lightweight decoder. Beyond this, we contribute two techniques and one empirical finding: (i) a self-distillation scheme that re-uses SAM3 itself, prompted with ground-truth boxes, as a teacher on the classes where it outperforms our own model; (ii) an image-level multi-scale test-time augmentation scheme that restores multi-scale inference for a fixed-input-size model by rescaling the image rather than the model input; and (iii) the finding that an aggressive photometric distortion from a winning 2025 GOOSE 2D entry, transplanted onto our pipeline, is its single largest source of improvement.

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