arXiv subjects

Abhishek Sharma

Publications and source records attributed to Abhishek Sharma.

At least 19 recordsLinked to original sources

arXiv2026

Programmable Droplets as Active Matter in a Reconfigurable Environment

Exploring systems that bridge the gap between non living and living matter requires a reconfigurable environment coupled to synthetic systems capable of autonomous motion, adaptive behaviour and persistent dynamics. Self propelled droplets provide a minimal platform for such life like behaviour, but quantitative measures capturing both persistence and adaptability to enable closed loop reconfiguration remain lacking. Here we introduce an autonomous closed loop robotic platform to characterise and dynamically program the autonomous behaviour of multicomponent oil in water droplets. We explored two chemical composition spaces, with and without external pH perturbations, analysing droplet behaviour in real time and iteratively selecting formulations. To quantify this behaviour, we introduce two descriptors, persistence and diversity. Persistence is quantified by mapping temporal dynamics in a speed shape complexity space, where we identify a persistence domain that distinguishes active from inactive droplets. Diversity captures spontaneous or environmentally induced variation in motion and the propensity of droplets to reorient or adapt under perturbation. We show that persistence and diversity are constrained by a behavioural trade off: high persistence limits diversity, whereas high diversity reduces persistent activity. Our results provide a quantitative framework for navigating and programming life like behaviours in active droplets and show how closed-loop experimentation can uncover organising principles in complex active matter.

cs.RO↗

arXiv2026

Probing dark sector with decihertz gravitational wave observations of compact binary inspirals in IndIGO-D

We investigate the potential of decihertz gravitational-wave (GW) observations of compact-binary inspirals to probe atomic dark matter (aDM) and its interactions through an ultralight massive dark photon. We consider a scenario in which neutron stars accumulate DM charged under a dark Abelian massive vector mediator, leading to an additional long-range Yukawa interaction between compact objects and associated dipolar radiation. In this framework, the GW waveform acquires characteristic corrections parameterized by the Yukawa coupling strength $α$, the dipole-radiation parameter $γ$, and a parameter related to the mediator mass $μ$. These corrections include a leading $-1$ post-Newtonian dipole contribution and finite-mediator-mass effects. We relate the aDM corrections to existing parameterized tests of general relativity and perform Bayesian parameter-estimation analyses for simulated binary neutron star (BNS) and neutron star-black hole (NSBH) systems incorporating the modified binary inspiral in a proposed space-based decihertz detector, named IndIGO-D. For a GW170817-like BNS with nonzero aDM couplings chosen near current observational bounds, we find that the dark-photon rest energy can be inferred with a $90\%$ credible uncertainty of $O(10^{-20})\,\mathrm{eV}$, conditional on a detectable dark-sector contribution. Analyses with weaker dark-sector interactions $(α\lesssim O(10^{-8}))$ show that the mediator mass remains unconstrained when both the Yukawa coupling and dipole-radiation contribution are negligible, but can be measured even for a negligible Yukawa coupling if the dipole-radiation contribution is sufficiently strong. Our results demonstrate that future decihertz GW observatories can serve as powerful laboratories for testing dark-sector physics and probing dark matter interactions in compact binaries through precision GW measurements.

gr-qc↗

arXiv2026

Probing Compact Binary Coalescences in the Decihertz GW Band with IndIGO-D

We present an astrophysical science-case study of IndIGO-D, a decihertz gravitational-wave mission concept, focusing on compact binary sources across the 0.1-10 Hz band bridging LISA and next-generation ground-based interferometers. For these science projections, we consider three spacecraft forming an L-shaped interferometer in a heliocentric orbit, with two orthogonal 1000 km arms sharing a common vertex. We calculate the time-dependent antenna response of this geometry, including modulation produced by the orbital motion of the constellation. We treat the strain-sensitivity curve supplied by the IndIGO-D collaboration as an input. For the assumed sensitivity, we estimate a BNS detection rate of $\sim76$-$767\,{\rm yr}^{-1}$. We compute horizon distances for different source classes and find that intermediate-mass black-hole binaries with masses $10^2$-$10^3\,M_\odot$ are detectable throughout their formation history, complementing the reach of LISA and terrestrial detectors. Binary neutron star systems are observable to a horizon redshift of $z\lesssim0.3$, allowing continuous multi-band coverage with Voyager-class interferometers from the decihertz regime to merger. A Bayesian parameter-estimation study of a GW170817-like binary shows that the sky localization area improves from $\sim21\,{\rm deg}^2$ at one month, $\sim1\,{\rm deg}^2$ at one week, down to $0.3\,{\rm deg}^2$ at six hours pre-merger. These sky areas are readily tiled by the Rubin Observatory, whose $9.6\,{\rm deg}^2$ field of view and r-band depth enable high-cadence, repeated coverage of GW170817-like kilonovae at this distance and beyond. IndIGO-D bridges the gap between millihertz and terrestrial observations, enabling early warnings on timescales from months to hours and enhancing the prospects for multi-band and multi-messenger discoveries. (abridged)

gr-qc↗

arXiv2026

Developing the Statistical Mechanics of Evolving Assembly Spaces

A gap exists in connecting biological evolution within the framework of statistical mechanics. It has been hoped that statistical mechanics would provide us with a general predictive principle for the emergence of novelty and complexity. However, the emergence of novel functional features initiated by stochastic processes remains difficult to describe, because these processes commonly arise outside near-equilibrium or linear-response regimes. To understand how environmental influences generate diverse, open-ended, complex objects by driving physical systems to specific regions of phase space over long spatial and temporal scales, a new approach to non-equilibrium thermodynamics is necessary. Here, we present the coupling of assembly theory (AT) with statistical mechanics as a framework that works within the laws of thermodynamics, but can contextualise the processes of novelty, complexity, and evolution within these laws. We map construction processes from physical space to assembly space (AS) and introduce the concept of an assembler, a machine that constructs objects contingent on previous states and is represented by bitwise operations. Using this approach, we apply principles from stochastic thermodynamics with AT to define selection within large combinatorial spaces1 and estimate the work required to create novel and complex objects. This approach allows us to represent complex processes, such as selection, within large combinatorial spaces as a set of rules for a memory-feedback loop followed by the assembler. This framework concretely defines the relationships between thermodynamics and information theory by introducing a novel aspect of physics that uses history and causal contingency to influence the probabilistic time evolution of complex systems.

cond-mat.stat-mech↗

arXiv2026

HoldSpec: A Machine-Checked Model and Conformance Suite for the Payment Authorization Hold Lifecycle

When a merchant authorizes a card and captures the funds later, the money sits in a state that neither party fully controls: held on the cardholder's account, not yet taken, and due to lapse on a deadline set by the card network. What a merchant may do to that hold, capture part of it, capture again, void it, raise it, let it expire, is business logic that every payment service provider implements slightly differently and none of them specifies formally. We give the lifecycle a state machine in TLA+, parameterized by a provider capability profile, and check eight safety invariants and three liveness properties over 6 profiles, five of them built from Stripe's and Adyen's published documentation. From the same machine we generate a conformance suite that runs black-box against a provider API. Against 61 seeded defects that are distinguishishable from a correct implementation, the generated suite detects 100%, where random sequences with the same call budget reach 56% and a hand-written integration suite reaches 69%; random sequences given ten times the budget still reach only 67%. Running the same suite between two providers turns the comparison into a search, and it returns short executable witnesses for divergences the two documentation sets imply but neither states, including one that breaks the common assumption that a single adapter interface can sit over both, because closing a hold and releasing its remainder is a capture on one provider and a cancel on the other. We report what we could not do: no provider sandbox credentials were available, so every result here concerns documented behavior, and the suite that would test the providers themselves is part of what we release.

cs.SE↗

arXiv2026

When Is a Reconciliation Break Real? A Temporal Correctness Contract for Streaming Financial Reconciliation

Reconciliation systems decide, continuously, whether money the ledger expected has arrived at the bank. On an asynchronous stream that decision is taken against partial evidence: a credit that has not appeared may be missing, or may simply not have been posted yet. Deployed pipelines settle this with a timeout or a watermark, and the exceptions are treated as findings. We give a temporal correctness contract that says when such a verdict is admissible, relative to a declared per-source lateness bound, and a checker for it that needs no ground truth. The contract separates three things an operator otherwise sees only as a rising break rate: the reconciler decided too early, an input broke the bound it declared, or the money really is gone. On workloads from a published payment simulator, the standard remedy for a stalled streaming join, an idleness timeout, introduces contract violations on 5.42% of obligations (95% CI [3.94, 6.92]) on traffic where the same job without it is exactly sound, and violations vanish once the timeout reaches the slow source's posting period, as predicted. Under a lateness shift, all four identity-based reconcilers we test raise spurious breaks on between 18.3% and 23.0% of the credits the bank did settle, while their contract violation rate stays at or below 1.01%: the checker attributes the loss to 240 bank-side and 413 ledger-side bound violations rather than to the reconcilers. Tracking one horizon per source leaves break latency 12.7% lower on average and halves its tail (p99 224 against 446 ticks) at equal soundness, though it is not cheaper at every cost setting and we report where it loses. An Apache Flink interval join, written the ordinary way, violates the contract on 14.9--20.1% of obligations. The contract is expressible as SQL over the tables a reconciliation system already keeps, agreeing with the reference implementation on all 4,395 verdicts.

cs.SE↗

arXiv2026

Identity-Conditioned Score Fusion for Open-Set Person Re-Identification

Robust person re-identification often combines complementary cues such as face, gait, and body shape. While adaptive fusion typically targets query quality, model strength also varies across identities. We introduce identity-conditioned score fusion, a framework that tailors weights to each gallery identity without training. By contrasting intra-identity consistency against cross-identity impostors, it extracts identity-specific profiles that couple with query-conditioned adaptation via a parameter-free rule. This widens the separation between true and false matches while preserving score calibration. Evaluations on three clothes-changing person re-identification benchmarks show that our method consistently outperforms statistical, rank-based, and learned baselines, achieving up to an 8.8% absolute reduction in the false non-identification rate and demonstrating the value of identity-conditioned fusion in open-set person re-identification.

cs.CV↗

arXiv2026

Non-Uniform Quantum Well and Barrier Thickness Engineering for Robust Ultra-High TER and Low RA Ferroelectric Tunnel Junctions

HfO$_2$-based ferroelectric tunnel junctions (FTJs) are promising candidates for scalable non-volatile memory, but simultaneously achieving a high tunneling electro-resistance ratio (TER) and a low resistance-area (RA) product remains challenging. To address this challenge, this work introduces non-uniform quantum well (QW) and barrier thickness engineering in HfO$_2$-based multi-QW FTJs using a self-consistent Preisach-based ferroelectric (FE) model integrated with the coherent and inelastic non-equilibrium Green's function (NEGF) formalism. The non-uniform well and barrier configuration produces a wide range of FTJ design landscapes due to closely spaced, broad resonant states in the low resistance state (LRS) and a larger separation in the high resistance state (HRS), resulting in strong polarization-dependent resonant transmission with TER reaching the order of $\mathbf{1\times10^{8}\%}$ and an LRS RA product as low as $\mathbf{1~Ω\cdot\mathrm{cm}^{2}}$ at read bias and in the presence of scattering. By incorporating self-consistent elastic scattering into the NEGF framework, we also show that elastic scattering can positively influence the TER and RA performance of non-uniform FTJs by progressively increasing the overlap of the closely spaced resonances. Overall, the results establish non-uniform QW and barrier thickness as an effective and robust design parameter for controlling resonant-state alignment and achieving a favorable combination of high TER and low RA in HfO$_2$-based multi-QW FTJs.

cond-mat.other↗

arXiv2026

CausalLoss-Fin: Attributing Financial-Agent Loss to Decisions and Infrastructure Faults

When an agent handling a payment exception loses money, the agent-step attribution methods this paper compares against will name one of its actions. They will do so even when a settlement message was dropped and the agent never had a chance: they intervene on agent actions and do not expose infrastructure faults as intervenable variables, so every dollar they explain is charged to a decision. We take a benchmark whose fault process is explicit and replayable, decompose each episode's realised delivery schedule into named, individually repairable messages, and intervene on both the agent's choices and the infrastructure's. A telescoping identity splits any policy's loss exactly three ways: an infrastructure effect, a policy differential against the best implementable policy, and a reference-policy residual. Two of the three can be negative, so none is a share; Shapley then divides the first into signed allocations over individual messages. One result is structural and needs no corpus: an agent-only baseline identifies no infrastructure cause, because its model contains no variable that could name one. What 545 planted episodes across 3 policies measure is the size of that consequence. It misfiles 100% of infrastructure episodes and charges $114,383.40 to the agent. Repairing what it names recovers 0.0% of the available loss; repairing a minimal sufficient set recovers 100.0%. Scoring messages one at a time is not merely imprecise: 27.8% (95% CI: 23.3--32.3%) of episodes do not decompose additively. We evaluate deterministic programmatic policies rather than language-model agents, which is what makes replay exact and which limits external validity to stochastic agents. The prevalence figures are properties of this generator, not field rates.

cs.AI↗

arXiv2026

Cat-Code-Protected Controlled Quantum Communication via Non-Local CNOT Gates over Star Quantum Networks

Controlled quantum communication enables secure state transfer between a sender and receiver with the assistance of one or more controllers. However, practical implementation over optical fibre networks is severely hindered by amplitude damping, which reduces fidelity exponentially with distance. We address this challenge by combining two powerful techniques: cat-state encoding for error correction and optimal non-local CNOT gates for distributed gate implementation. The protocol eliminates the need for the physical position qubit itself to travel through the optical fibre, reducing damping events. We show, through density-matrix simulations, that our cat-code-protected protocol with a non-local CNOT operation achieves higher fidelity at 50 km, significantly outperforming the standard protocol. We analyse the protocol's security against beam-splitter attacks and show that CHSH tests provide security against beam-splitter attacks on the distributed entanglement resource despite the cat code's error correction. Our results establish that cat-code-protected controlled quantum communication is feasible with current technology and structurally extensible to multiple controllers, providing a theoretical framework for studying error-corrected controlled quantum communication over long-distance star quantum networks.

quant-ph↗

arXiv2026

FinalityBench: An Effect-Level Benchmark for Agent Decisions Under Delayed and Conflicting Financial Finality

A merchant's payment processor, ledger, ERP and bank feed are updated by messages that get delayed, duplicated, dropped and reordered, so for minutes at a time the four hold contradictory beliefs about the same order. An agent resolving the exception must decide whether to ship goods, re-submit a capture, refund or wait, knowing some of those cannot be undone. We present FinalityBench, an executable benchmark for that decision. It keeps a hidden canonical event log and derives each system's view from a separately faulted delivery stream, so disagreement follows from specified fault semantics rather than being authored. Grading is on executed monetary effects: an episode is scored by the merchant's terminal economic position, relative to a privileged reference told when the pending capture resolves. The corpus of 321 tasks includes 45 twin pairs (90 tasks): tasks whose four system views are identical at the decision instant, whose authoritative probes both return unknown, and whose eventual correct dispositions differ. That snapshot indistinguishability is checked under every evaluation seed rather than assumed; equivalence over all interaction traces is not claimed. Over 14,445 graded episodes from nine programmatic policies, ranking by single-task accuracy and by paired loss disagree in 7 places: a ship-on-first-sign policy is second-best by accuracy at 65.7% and worst in the suite by paired loss, because it cannot tell the two members apart. A runtime gating irreversible actions on an authoritative finality probe reaches 85.4% and, unlike every polling policy, loses nothing to pass^5; its residual loss is almost entirely one archetype, which prices finality information directly. Language models reach the same exact rate as the hand-written gate on a stratified subset, lose about twice as much money, and discover the finality-gating strategy without being told it.

cs.AI↗

arXiv2026

Interpretable statistical feature engineering for early disruption prediction in the short pulse ADITYA tokamak

Reliable early disruption prediction is critical for the safe operation and real-time control of tokamaks. However, machine learning based prediction frameworks have predominantly targeted medium and long pulse devices, with comparatively limited attention given to short pulse tokamaks where available warning time is inherently constrained. In this work, an interpretable machine learning framework is developed for feature engineering and early prediction of disruptions in the ADITYA using the initial plasma evolution information, prior to the activation of the negative converter of the ohmic transformer power supply. Statistical descriptors comprising the mean, variance, skewness, kurtosis and wavelet energy entropy are extracted from routinely available plasma diagnostics over different operation time windows. Decision tree based feature selection is employed to identify physically meaningful disruption precursors and to reduce feature dimensionality. These selected features are used to train a random forest classifier. The proposed framework achieves stable predictive performance across different analysis windows, with a maximum ROC-AUC of 0.87 for 0-35 ms and 0-40 ms windows. Comparable and in some cases improved, performance is obtained using the reduced feature set, demonstrating that the selected statistical descriptors retain the essential information required for disruption prediction. The proposed methodology provides an interpretable and computationally efficient framework for real time disruption prediction in short pulse tokamaks and establishes that carefully engineered statistical descriptors can effectively replace raw time series inputs for early disruption prediction, thereby offering a practical pathway toward real time plasma control in short pulse tokamaks similar to ADITYA and ADITYA-U.

physics.plasm-ph↗

arXiv2026

Domain-Growth Kinetics and Scaling Laws Governing Pulse-Driven Accumulative Polarization Switching in HZO

Accumulative polarization switching driven by sequential sub-coercive electric-field pulses offers a promising route toward low-power ferroelectric memories and neuromorphic devices. However, the kinetic regimes governing this nonequilibrium process remain poorly understood. Here, we employ a phase-field model based on the time-dependent Landau-Ginzburg formalism to investigate pulse-driven accumulative switching in ferroelectric HZO. By systematically varying the initial domain configuration, pulse amplitude, pulse-on time, and pulse-off time, we establish a quantitative link between microscopic domain-wall dynamics and macroscopic polarization accumulation. We show that the effective switched-domain radius follows distinct scaling regimes characterized by the local kinetic exponent. Initially, a local exponent greater than 1 indicates superlinear domain growth driven by enhanced irreversible domain-wall propagation under successive pulses. As switching progresses, a local exponent close to unity marks steady self-similar growth, whereas a local exponent less than 1 signifies decelerating dynamics caused by geometric confinement, depletion of switchable polarization, and relaxation-induced back switching. The transition between these regimes is governed by the competition between field-driven excitation during the pulse-on interval and spontaneous relaxation during the pulse-off interval. The initial domain geometry further influences this transition. Increasing the pulse amplitude or pulse-on duration extends the superlinear regime, whereas longer pulse-off times promote relaxation and suppress accumulation. These findings establish a unified scaling framework for pulse-driven accumulative switching, providing quantitative insight into nonequilibrium ferroelectric domain evolution and design guidelines for HZO-based memory and neuromorphic devices.

cond-mat.mtrl-sci↗

arXiv2026

Elucidating the Size of Chemical Space with Assembly Theory

Chemical space is unimaginably vast with common heuristic estimates suggesting that there are ca. 10^60 'drug-like' molecules possible below a molecular mass of 500 Da. However, these estimates largely ignore the structural and synthetic complexity of the molecules enumerated. Here we present a first-principles estimate of the size of chemical space using the Assembly Theory, which quantifies the amount of causation required to form a molecule, captured in the assembly Index. This is a measurable molecular complexity measure derived from the minimum number of recursive bond-joining operations required to construct a molecular graph. Assembly Theory partitions chemical space into levels defined by Assembly Index, allowing bounds to be placed on its growth as molecular complexity increases. We show that chemical space (the accumulated Assembly Index level sets) grows at least super-exponentially, and at most, double-exponentially with respect to the Assembly Index. Using the GDB-13 database as a reference for growth-rate estimation, we model how chemical space expands under increasing complexity and contracts under structural constraints, including atom and bond types, number of rings, ring size, and chemical motifs. Under constraints comparable to standard drug-like estimates, including molecular mass below 500 Da, our analysis yields a chemical space of approximately 10117 molecules at Assembly Index 25. Finally, we constrain chemical space by biologically relevant motifs and identify structurally relevant molecules near the accessible boundaries of these assembly-defined spaces.

physics.chem-ph↗

arXiv2026

BADGER: Bridging Agentic and Deterministic Evaluation for Generative Enterprise Reasoning

Enterprise AI systems that translate natural language into SQL queries and orchestrate multi-step agentic reasoning pipelines require evaluation approaches fundamentally different from academic benchmarks. Spider and BIRD established execution-accuracy protocols; G-Eval and RAGAS advanced LLM-based assessment; and recent work such as Spider 2.0, BEAVER, and BIRD-Interact has begun to address enterprise and agentic dimensions. No single framework unifies text-to-SQL assessment with agentic behavior evaluation into a production-grade pipeline calibrated against human expert judgment. We present BADGER, developed at Merkle, a unified evaluation framework integrating text-to-SQL assessment with agentic behavior evaluation. BADGER offers three contributions. First, LLM-assisted SQL component extraction extending Spider methodology to handle CTE-heavy, dialect-specific SQL. Second, a hybrid execution accuracy metric (Hybrid-EX) resolving column-aliasing and numeric-tolerance brittleness by using an LLM to infer structural alignments before deterministic cell-level scoring. Validated on 150 human-annotated industry queries, Hybrid-EX achieves Cohen's kappa=0.717 [95% CI: 0.600-0.822] (Substantial agreement) and 87.3% balanced accuracy, outperforming all six competing frameworks (Delta-kappa: 0.322-0.502, all p<=0.001). Third, an enterprise agentic evaluation suite assembling RAGAS, G-Eval, and agent benchmark metrics into a unified pipeline; Excess Tool Usage is the sole novel element. BADGER runs entirely within the client's governed data environment, supports configurable LLM judge backends, and enables rapid prototyping of client-specific judges and metrics, serving as a continuous evaluation backbone rather than a one-time quality gate.

cs.AI↗

arXiv2026

Quantifying Potential Observation Missingness in Inverse Reinforcement Learning

Inverse reinforcement learning (IRL), which infers reward functions from demonstrations, is a valuable tool for modeling and understanding decision-making behavior. Many variants of IRL have been developed to capture complexities of human decision-making, such as subjective beliefs, imperfect planning, and dynamic goals. However, an often-overlooked issue in real-world behavioral datasets is that the recorded data may be missing observations that were available to the original decision-maker. In use-inspired settings such as healthcare, this can make expert actions appear suboptimal, even when they were near-optimal given the information available at the time. As a result, the rewards learned by standard IRL may be misleading. In this paper, we identify the minimal perturbations to the recorded observations needed for the expert's actions to appear optimal. We develop a practical algorithm for this problem and demonstrate its utility for quantifying the possible extent of missing observations in behavioral datasets through extensive experiments on synthetic navigation tasks, a cancer treatment simulator, and ICU treatment data.

cs.LG↗

arXiv2026

Bayesian Inverse Transition Learning: Learning Dynamics From Near-Optimal Trajectories

We consider the problem of estimating the transition dynamics $T^*$ from near-optimal expert trajectories in the context of offline model-based reinforcement learning. We develop a novel constraint-based method, Inverse Transition Learning, that treats the limited coverage of the expert trajectories as a \emph{feature}: we use the fact that the expert is near-optimal to inform our estimate of $T^*$. We integrate our constraints into a Bayesian approach. Across both synthetic environments and real healthcare scenarios like Intensive Care Unit (ICU) patient management in hypotension, we demonstrate not only significant improvements in decision-making, but that our posterior can inform when transfer will be successful.

cs.LG↗

arXiv2026

Flocking through a sea of rods

We investigate the collective behavior of motile rods immersed in a monolayer of apolar rods confined between vertically vibrating plates using numerical simulations. We uncover an antidiffusive instability whereby motile rods segregate from the apolar medium and form flocks whose size increases with the medium concentration. Remarkably, enhanced segregation leads to a reduction of the global polar order. The flock structure is strongly influenced by the anisotropy of the medium rods. For small aspect ratios, the flocks are elongated perpendicular to the mean direction of motion, whereas for larger aspect ratios, they elongate along the direction of motility. We rationalize the emergence of segregation-induced disorder using a minimal mean-field model.

cond-mat.soft↗