arXiv subjects

Abhinav Agarwal

Publications and source records attributed to Abhinav Agarwal.

11 recordsLinked to original sources

arXiv2026

Certified local rank and uniqueness barriers for a 48-term matrix-multiplication decomposition

We study replacements in fixed bilinear tensor decompositions, counting changes to complete rank-one summands, including output factors. The shortening frontier records the maximum rank defect of a fixed-size subset and determines the minimum length attainable within a change budget. For the rational 48-term Li--Wang--Hu decomposition \(D(2)\) of \(4\times4\) matrix multiplication over \(\mathbb{C}\), we prove rank radius at least 12, strong radius exactly 11, and border radius at least 8. Every shorter complex decomposition therefore changes at least thirteen original summands. An exact rational twelve-term replacement attains the equal-length barrier. The proofs combine exhaustive support reductions with saturated projected kernels and zero-corner completion arguments controlling arbitrary minimal competitors. A reduced-incidence argument transfers kernel certificates to tensor-space neighborhoods. A Laurent normal form gives strong radius exactly 11 for the sixteen-term core at every nonzero complex parameter. On a nonempty Zariski-open subset of the actual parameter curve, the rank radius is at least 12, the strong radius exactly 11, and the border radius at least 8. We also prove incomparability of the full Kothari--Moitra--Wein sufficient criterion and the Sylvester-equipped kernel criterion. These results describe local decomposition structure rather than a new rank bound for full matrix multiplication.

cs.SC↗

arXiv2026

Revisiting Open-Loop Execution in Robotics: Toward Reactive, Higher-Performing Policies

Action chunking --- the practice of predicting a sequence of actions and executing a prefix open-loop --- has emerged as a key enabler of recent progress in imitation learning for robotic manipulation. However, executing long open-loop prefixes reduces reactivity, limiting policies' ability to correct for errors. Further, the mechanisms underlying these performance benefits remain poorly understood: prior works cite mitigating compounding errors, absorbing inference latency, or smoothing motions, but provide limited controlled evidence or guidance for preserving reactivity. In this work, we argue that long open-loop execution primarily helps short-context policies imitate "non-Markovian demonstrations". Across four simulation and two real-world tasks, we show that expert non-Markovianity strongly shapes the relationship between task success and open-loop execution horizon. Further, we investigate the impact of compounding errors --- the prevailing explanation for long open-loop execution in prior work --- and find that while they matter, expert non-Markovianity has a much stronger impact in our experimental setting. Finally, we show that when policies are provided with a sufficiently long context, open-loop execution is no longer beneficial and the most reactive, closed-loop policies perform best. While imitation learning has seen great success using long open-loop execution, our findings motivate long-context, reactive policies as a more principled and performant paradigm.

cs.RO↗

arXiv2026

Thermodynamic and electrical transport properties of the half-Heusler plumbide TbAuPb

Structural, thermodynamic and electrical transport properties of TbAuPb were investigated on single crystals. The compound was found to crystallize with the cubic MgAgAs-type structure characteristic of half-Heusler materials. It orders antiferromagnetically at TN = 5 K and undergoes a transition into a different antiferromagnetic phase emerging in high magnetic fields. Electrical transport in TbAuPb exhibits a multiband character, with a predominance of hole-like carriers. Angular magnetoresistance evolves systematically with applied magnetic field and changes its symmetry near the spin-reorientation transition, highlighting strong coupling between the charge transport and the magnetic order. The results of first-principles calculations indicate that TbAuPb is a band inverted semimetal in the non-magnetic state, which becomes topologically trivial in the field-induced ferromagnetic state.

cond-mat.str-el↗

arXiv2026

Training and Evaluating Diffusion Policies with Long Context Lengths

Imitation learning has enabled highly-dexterous robotic manipulation from RGB observations. Policies trained with these methods, however, typically condition robot actions on only a short history of observations. These policies cannot solve tasks that require memory and can get stuck repeatedly executing the same failing motions. In this work, we first benchmark policy performance as context length is incrementally increased from short to long, across a spectrum of tasks with varying local stability and memory requirements, and in multiple data regimes. To our knowledge, this is the first study to investigate context length for Diffusion Policies at this level of detail. Our results challenge prior claims: naively scaling context length is not as brittle as advertised in literature. With an appropriate conditioning method and denoising backbone (UNet+Cross-Attention), single-task policies achieve high success rates on many tasks in the usual data regime even with naive scaling. Next, we propose a training algorithm to jointly train policies at multiple context lengths, further reducing the sample complexity of long-context learning. Finally, we apply our findings to re-evaluate some previously proposed solutions to long-context imitation learning.

cs.RO↗

arXiv2026

Refute-or-Promote: An Adversarial Stage-Gated Multi-Agent Review Methodology for High-Precision LLM-Assisted Defect Discovery

LLM-assisted defect discovery has a precision crisis: plausible-but-wrong reports overwhelm maintainers and degrade credibility for real findings. We present Refute-or-Promote, an inference-time reliability pattern combining Stratified Context Hunting (SCH) for candidate generation, adversarial kill mandates, context asymmetry, and a Cross-Model Critic (CMC). Adversarial agents attempt to disprove candidates at each promotion gate; cold-start reviewers are intended to reduce anchoring cascades; cross-family review can catch correlated blind spots that same-family review misses. Over a 31-day campaign across 7 targets (security libraries, the ISO C++ standard, major compilers), the pipeline killed roughly 79% of 171 candidates before advancing to disclosure (retrospective aggregate); on a consolidated-protocol subset (lcms2, wolfSSL; n=30), the prospective kill rate was 83%. Outcomes: 4 CVEs (3 public, 1 embargoed); LWG 4549 accepted to the C++ working paper; 5 merged C++ editorial PRs; 3 compiler conformance bugs; 8 merged security-related fixes without CVE; an RFC 9000 errata filed under committee review; and 1+ FIPS 140-3 normative compliance issues under coordinated disclosure -- all evaluated by external acceptance, not benchmarks. The most instructive failure: ten dedicated reviewers unanimously endorsed a non-existent Bleichenbacher padding oracle in OpenSSL's CMS module; it was killed only by a single empirical test, motivating the mandatory empirical gate. No vulnerability was discovered autonomously; the contribution is external structure that filters LLM agents' persistent false positives. As a preliminary transfer test beyond defect discovery, a simplified cross-family critique variant also solved five previously unsolved SymPy instances on SWE-bench Verified and one SWE-rebench hard task.

cs.CR↗

arXiv2026

Anomalous magnetotransport in the single-crystalline half-Heusler antiferromagnet ErPdSb

We report the thermodynamic and magnetotransport properties of the half-Heusler antimonide ErPdSb, studied on single-crystalline samples in wide ranges of temperature and magnetic fields. The compound was found to order antiferromagnetically at 1.2 K. In the paramagnetic state, it shows semimetallic behavior with a broad hump in the temperature-dependent electrical resistivity around 70 K. The results of ab initio calculations of the electronic structure of ErPdSb indicated a bulk insulating nature. In small magnetic fields the magnetoresistance is driven by a weak antilocalization effect, while in strong fields it is negative and describable by the deGennes-Friedel formalism. The Hall effect data indicated that holes are the dominant charge carriers. At 2 K, the Hall conductivity exhibits a sizable anomalous contribution, which is obscured by multiband effects at higher temperatures. The angular magnetoresistance shows unusual features as functions of magnetic field and temperature, pointing to a possible field-induced reconstruction of the Fermi surface.

cond-mat.str-el↗

arXiv2026

Magnetic and electrical transport properties of the single-crystalline half-Heusler antiferromagnet DyNiSb

High-quality single crystals of the half-Heusler compound DyNiSb were investigated for their low temperature thermodynamic and magnetotransport properties. Magnetic susceptibility, heat capacity, and electrical resistivity measurements revealed two distinct magnetic phase transitions at TN1 = 7.3 K and TN2 = 3.4 K, contrasting with previous reports on polycrystalline samples, which identified only a single transition near TN2 . Moreover, the studied samples were found to exhibit Metal like conductivity, at odds with a semiconducting behavior reported for the polycrystals. Magnetoresistance measurements performed in both transverse and longitudinal configurations revealed in small magnetic fields a weak antilocalization effect that diminishes with increasing temperature, giving way to a positive, monotonic magnetoresistance at high temperatures. Angular-dependent resistivity studies showed a crossover from fourfold to twofold symmetry with increasing magnetic-field strength, suggesting a field-induced reconstruction of the Fermi surface. Our findings highlight a complex magnetic and electrical transport behavior in DyNiSb, highly sensitive to structural disorder and easily tunable by external magnetic field.

cond-mat.str-el↗

arXiv2025

Field-induced reversible phase transition and negative differential resistance in In2Se3 ferroelectric semiconducting FETs

Indium selenide (In2Se3), a ferroelectric semiconductor, offers a unique platform for multifunctional nanoelectronics owing to the interplay between polarization dynamics, interlayer sliding, and structural polymorphism. Ferroelectric semiconductor field-effect transistors (FeS-FETs) provide an ideal architecture to harness this coupling. Here, we demonstrate gate-tunable negative differential resistance (NDR) with high peak-to-valley ratios and hysteretic output conductance in In2Se3 FeS-FETs. Combining high-resolution electron microscopy with electrical transport measurements, we attribute the NDR to a field-induced, volatile phase transition from a low-resistance alpha-2H phase to a high-resistance state. Atomic scale ex-situ imaging reveals that in-plane electric fields (Vd) drive interlayer sliding, rotational misalignments that generate Moire patterns, and intralayer shear-together producing stress induced phase transitions. Out-of-plane field however results in robust non-volatile polarization switching. These mechanistic insights highlight both the promise of two dimensional ferroelectric devices for multifunctional nanoelectronics and alternative computing paradigms, and the intrinsic limitations of In2Se3 field-effect transistors for conventional ferroelectric memory applications.

cond-mat.mtrl-sci↗

arXiv2025

Empirical Analysis of Sim-and-Real Cotraining of Diffusion Policies for Planar Pushing from Pixels

Cotraining with demonstration data generated both in simulation and on real hardware has emerged as a promising recipe for scaling imitation learning in robotics. This work seeks to elucidate basic principles of this sim-and-real cotraining to inform simulation design, sim-and-real dataset creation, and policy training. Our experiments confirm that cotraining with simulated data can dramatically improve performance, especially when real data is limited. We show that these performance gains scale with additional simulated data up to a plateau; adding more real-world data increases this performance ceiling. The results also suggest that reducing physical domain gaps may be more impactful than visual fidelity for non-prehensile or contact-rich tasks. Perhaps surprisingly, we find that some visual gap can help cotraining -- binary probes reveal that high-performing policies must learn to distinguish simulated domains from real. We conclude by investigating this nuance and mechanisms that facilitate positive transfer between sim-and-real. Focusing narrowly on the canonical task of planar pushing from pixels allows us to be thorough in our study. In total, our experiments span 50+ real-world policies (evaluated on 1000+ trials) and 250 simulated policies (evaluated on 50,000+ trials). Videos and code can be found at https://sim-and-real-cotraining.github.io/.

cs.RO↗

arXiv2022

Stronger Generalization Guarantees for Robot Learning by Combining Generative Models and Real-World Data

We are motivated by the problem of learning policies for robotic systems with rich sensory inputs (e.g., vision) in a manner that allows us to guarantee generalization to environments unseen during training. We provide a framework for providing such generalization guarantees by leveraging a finite dataset of real-world environments in combination with a (potentially inaccurate) generative model of environments. The key idea behind our approach is to utilize the generative model in order to implicitly specify a prior over policies. This prior is updated using the real-world dataset of environments by minimizing an upper bound on the expected cost across novel environments derived via Probably Approximately Correct (PAC)-Bayes generalization theory. We demonstrate our approach on two simulated systems with nonlinear/hybrid dynamics and rich sensing modalities: (i) quadrotor navigation with an onboard vision sensor, and (ii) grasping objects using a depth sensor. Comparisons with prior work demonstrate the ability of our approach to obtain stronger generalization guarantees by utilizing generative models. We also present hardware experiments for validating our bounds for the grasping task.

cs.RO↗

arXiv2020

Architectural Analysis of FPGA Technology Impact

The use of high-level languages for designing hardware is gaining popularity since they increase design productivity by providing higher abstractions. However, one drawback of such abstraction level has been the difficulty of relating the low-level implementation problems back to the original high-level design, which is paramount for architectural optimization. In this work (developed between April 2013 and April 2014), we propose a methodology to analyze the effects of technology over the architecture, and to generate architectural-level area, delay and power metrics. Such feedback allows the designer to quickly gauge the impact of architectural decisions on the quality of generated hardware and opens the door to automatic architectural analysis. We demonstrate the use of our technique on three FPGA platforms using two designs: a Reed-Solomon error correction decoder and a 32-bit pipelined processor implementation.

cs.AR↗