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Abhijit Chakraborty

Publications and source records attributed to Abhijit Chakraborty.

At least 19 recordsLinked to original sources
arXiv2026

Typed Federated Artifacts for the Agentic Web:Sharing Tool-Routing Knowledge Across Frozen,Heterogeneous LLM Agents

An open, networked web will allow agents to run frozen models from multiple vendors, keep their history private, and teach each other which tool to call and when. Flat text (prompts, example pools) makes it difficult for the protocol to distinguish between noise statistics, merging rules, and documentation. Weights and adapters cannot transfer that knowledge between platforms. We suggest sharing typed federated artifacts, schema-validated objects with well-defined fields for per-field privacy (described here, but measured), dispute resolution, and cross-model transfer, and instantiating them as SYNAPSE1, a common tool-routing knowledge. After deleting 192 garbage entries and 1,916 training items that duplicate or almost duplicate test queries, a federated compendium routes within 1.1 points of a centralized one at 20 MB of JSON per client each round on StableToolBench (3,180 tools). The same experience merged and shown to the router as typed fields rather than one flat string is worth 8.5 points on clean data and 7.4 under 60% injected contradiction. Crossing merge and rendering shows the halves are inseparable (the typed merge shown flat is the worst arm), while three conflict policies are indistinguishable, so the conflict log that motivated this work is not the On τ-bench retail, each compendium arm improves GPT-4o agents' per-step tool-call accuracy by at least 6.7 points, attributed to format rather than federated experience. Two cautionary findings conclude the paper: on a topic-labeled math proxy and StableToolBench, a TF-IDF classifier over the same labeled experience beats every LLM routing arm (by 48 and 26 points, mostly retrieval recall) because the benchmark's pool holds labeled queries for every supposedly unseen tool and every test query verbatim before our filter. It cannot measure routing to tools without labels, which routing exists for.

cs.CL↗
arXiv2026

Time-dependent weighted directed networks of cryptocurrency interaction from high-frequency returns

We investigate the evolving structure of interactions in cryptocurrency markets using a network-based framework constructed from high-frequency price data spanning 2020-2025. Directed and weighted networks are constructed from statistically significant Granger causal relationships between cryptocurrency log-returns, enabling us to quantify the flow of influence across assets. We find that normalized returns exhibit heavy-tailed distributions, consistent with the presence of large intermittent fluctuations and in line with stylized facts of financial markets. The resulting networks display pronounced heterogeneity in link weights and nodal strengths, indicating that a small subset of cryptocurrencies contributes disproportionately to market dynamics. By ranking cryptocurrencies based on their nodal out-strength, we uncover a dynamically evolving hierarchy of influence. Ethereum consistently emerges as the most influential asset, while Bitcoin shows a gradual decline in its relative importance. The ranking structure exhibits substantial temporal variability, with multiple cryptocurrencies entering and exiting the top positions over time. Our findings reveal a highly competitive and non-stable organization of the cryptocurrency ecosystem.

q-fin.TR↗
arXiv2026

Sectoral inter-dependencies drive the loss of structural balance in signed financial networks

Signed graphs provide an effective architecture for portraying a system in which cooperation and conflict coexist. Emerging from the concept of balance in psychological sciences, they have found applications across several domains. Financial markets are one such example that can be modeled using signed networks, where assets exhibit correlations in price movements. During periods of systemic risk, such a signed financial network shows a loss of balance, which has been consistently demonstrated. Here, we explore how this structural imbalance is distributed across scales within the financial network, revealing its mesoscopic origin. Adopting the framework of structural balance theory, we use a measure of polarization based on triadic motifs to investigate the distribution of structural imbalance across varying sectoral scales. We analyze the temporal evolution of global polarization and its sectoral constituents using longitudinal data derived from the S&P 500 index. By decomposing global polarization into intra-sectoral and inter-sectoral constituents, we show that structural imbalance arises predominantly from interactions between sectors rather than within them during periods marked by systemic risk. We employ randomization protocols to confirm that observed imbalance configurations are statistically significant and not artifacts of lower-order interactions. We derive a regression equation demonstrating that the variance in global polarization is well explained by macroeconomic variables, indicating that low levels of global polarization during economic crises are driven by compounding pressures from supply chain disruptions and inflation uncertainty. Collectively, these findings provide a quantitative framework for understanding how localized sectoral conflicts propagate across the financial network and contribute to large-scale structural instability during periods of economic crisis.

physics.soc-ph↗
arXiv2026

JobMatchAI-An Intelligent Job Matching Platform Using Knowledge Graphs, Semantic Search and Explainable AI

Recruiters and job seekers rely on search systems to navigate labor markets, making candidate matching engines critical for hiring outcomes. Most systems act as keyword filters, failing to handle skill synonyms and nonlinear careers, resulting in missed candidates and opaque match scores. We introduce JobMatchAI, a production-ready system integrating Transformer embeddings, skill knowledge graphs, and interpretable reranking. Our system optimizes utility across skill fit, experience, location, salary, and company preferences, providing factor-wise explanations through resume-driven search workflows. We release JobSearch-XS benchmark and a hybrid retrieval stack combining BM25, knowledge graph and semantic components to evaluate skill generalization. We assess system performance on JobSearch-XS across retrieval tasks, provide a demo video, a hosted website and installable package.

cs.AI↗
arXiv2026

An efficient algorithm for approximate shadow Hamiltonian simulation

We propose an efficient algorithm based on shadow Hamiltonian simulation to approximately simulate the real-time dynamics of observables under time-independent Hamiltonians. Shadow Hamiltonian simulation works at the level of the operator algebra generated by the observables through commutators with the Hamiltonian. Exactly encoding the quantum state in this picture is generally inefficient for interacting systems due to the exponential growth of the operator algebra. Our algorithm overcomes this bottleneck by systematically identifying the elements of the algebra most relevant to the target observables. This targeted approach is a controlled approximation that yields a highly efficient quantum state encoding that substantially reduces the size of the qubit register required to perform the time evolution using the shadow Hamiltonian. We propose two main pruning schemes, one based on a predefined operator basis and another on a constructed Krylov basis. We also present a hybrid scheme that builds a Krylov basis within a pruned algebra in the predefined basis. We benchmark our algorithm using lattice spin systems in one and two dimensions, for both one- and higher-point correlators as observables.

quant-ph↗
arXiv2026

Understanding eccentric temperate giants: an in-depth study of the architecture and stellar obliquity of the TOI-2134 system

We revisit the TOI-2134 planetary system with three new high-cadence TESS sectors and 98 more spectra. This new analysis confirms the two orbiting planets by simultaneously modelling a total of eight sectors of corrected TESS photometry and 280 HARPS-N and SOPHIE radial velocities: an inner mini-Neptune in a near-circular $9.229198\pm0.000003$ days orbit, and an outer temperate sub-Saturn orbiting with a $95.852840\pm0.000042$ days period and eccentricity of $0.31\pm0.01$. The masses and radii of the planets were computed to be $9.37\pm0.54$ Me and $2.735\pm0.068$ Re for planet b, and $58.3\pm1.9$ Me and $7.35\pm0.18$ Re for planet c. The new data not only improves the detection significance and precisions on the planetary orbits, but also breaks the original multimodality in the eccentricity solution for the outer planet. We also detect a long-term trend in the radial velocity data, which we attribute to a stellar magnetic cycle. We investigate the spin-orbit alignment of the system via observations of the Rossiter-McLaughlin effect for TOI-2134~b with EXPRES and TOI-2134~c with PARAS-2. No RM effect was detected for planet b, but we find a 4.7$σ$ detection of a $59\pm31^{\circ}$ obliquity for planet c. Finally, we examine the architecture of the system, assess its completeness, investigate the planetary interior, and their suitability for follow-up atmospheric analysis.

astro-ph.EP↗
arXiv2026

TOI-6884b: A low-mass brown dwarf transiting a slightly evolved star

We report the discovery of a low-mass transiting brown dwarf orbiting TOI-6884 (TIC~156514476, $T_{\rm mag}=11.4$) from NASA's \textit{Transiting Exoplanet Survey Satellite} (\textit{TESS}) mission. The \textit{TESS} light curves initially suggested an orbital period of $\sim$14.42~days; however, our high-precision ground-based radial velocity measurements and multi-epoch time-series photometry reveal this to be a harmonic alias. We determine the true orbital period to be $4.808264^{+0.000015}_{-0.000014}$~days and confirm the substellar nature of the companion. TOI-6884b has a mass of $26.32^{+0.98}_{-0.93}\,M_{\mathrm{J}}$, a radius of $0.927^{+0.51}_{-0.52}\,R_{\mathrm{J}}$, and resides on a nearly circular orbit ($e=0.067^{+0.010}_{-0.012}$). Its host star is a late F-type slightly evolved star with $M_\star = 1.410^{+0.075}_{-0.069}\,M_\odot$,\msun, $R_\star = 1.840^{+0.072}_{-0.073}\,R_\odot$, $\log{g} = 4.057^{+0.045}_{-0.039}$, $[{\rm Fe/H}] = 0.094^{+0.073}_{-0.068}$~dex, and $T_{\rm eff}=6330^{+180}_{-160}$,\mathrm{K}$. TOI-6884b is a key addition to the small population of well-characterized transiting brown dwarfs orbiting host stars that have evolved off the main sequence. The detection of such systems will contribute to our understanding of the dynamical histories and structural evolution of short-period substellar companions around evolved stars.

astro-ph.EP↗
arXiv2026

DeALOG: Decentralized Multi-Agents Log-Mediated Reasoning Framework

Complex question answering across text, tables and images requires integrating diverse information sources. A framework supporting specialized processing with coordination and interpretability is needed. We introduce DeALOG, a decentralized multi-agent framework for multimodal question answering. It uses specialized agents: Table, Context, Visual, Summarizing and Verification, that communicate through a shared natural-language log as persistent memory. This log-based approach enables collaborative error detection and verification without central control, improving robustness. Evaluations on FinQA, TAT-QA, CRT-QA, WikiTableQuestions, FeTaQA, and MultiModalQA show competitive performance. Analysis confirms the importance of the shared log, agent specialization, and verification for accuracy. DeALOG, provides a scalable approach through modular components using natural-language communication.

cs.CL↗
arXiv2026

Direct Optical Evidence of Late-Stage Infall in AB Aurigae: A Stagnant [O I] Reservoir and a Crushed Magnetosphere

Massive planet-carved cavities in transition disks should theoretically throttle inward gas transport, challenging our understanding of how central stars maintain vigorous accretion. To investigate how macro-scale late-stage infall traverses these gaps, we present multi-epoch, extreme-resolution (R ~ 107,000) PARAS-2 optical spectroscopy of the benchmark Herbig Ae system AB Aurigae. By resolving the kinematics of H-alpha, He I 5876, [O I] 6300, 6363, and Na I D, we map the innermost accretion environment. We find that the [O I] emission is centered near the stellar rest velocity with symmetric broadening of ~ 35 km/s. Restricted to T <= 3800 K, this profile traces a stagnant, gravitationally bound Keplerian gas reservoir at ~ 1 au. Therefore, it provides strong optical evidence that late-stage infall accumulates in an inner gas reservoir and subsequently feeds the innermost dust cavity. From this reservoir, gas is transported inward and crashes onto the star, driving a highly active accretion rate of dM/dt ~ 4 x 10^-7 M_sun/yr. The associated ram pressure crushes the stellar magnetosphere to R_mag ~ 1.2 R_star, which explains the restricted He I free-fall velocities and the highly variable inner wind. We also isolate a stable, slow H-alpha wind component, likely tracing an extended photoevaporative disk wind.

astro-ph.SR↗
arXiv2026

Synapse: Federated Tool Routing via Typed Compendium Artifacts

The unit of collaboration in federated learning determines what guarantees are even expressible. Flat units like weights, prompts, raw examples, carry no type signature on which privacy, conflict resolution, or cross-model transfer can dispatch as well-defined operations. We propose typed federated artifacts: schema validated objects whose declared field structure makes per field differential privacy, schema aware merging, and cross architectural transfer first-class operations rather than heuristic approximations. We instantiate this as SYNAPSE, a compendium for federated tool routing across clients with frozen, heterogeneous LLMs and no shared data or weights which is a setting flat units cannot handle without either leaking gradients or discarding structure. The compendium admits a typed merge operator with field wise conflict resolution, a formal DP guarantee on numeric metadata, and conditional retrieval distortion and routing-stability results empirically characterized on five distributions, including one where the contraction premise fails. A single compendium transfers across four LLM families (LLaMA 3.18B,LLaMA 3.2-3B, Mistral 7B, GPT 4o) with approximately 2 pt loss, a capability weight-sharing federation cannot provide without architectural matching.

cs.AI↗
arXiv2026

CaRE Compute-aware Remasking Evaluation Protocol for Masked Diffusion Language Models

Masked diffusion language models (MDLMs) are advancing rapidly, yet the evaluation standards needed to reliably interpret their progress have not kept pace. Despite MDLMs becoming competitive with autoregressive language models, seven recent remasking papers evaluate under incompatible settings, varying nominal step counts, metrics, and sampling temperatures without jointly controlling these factors, rendering their strategy rankings largely incomparable and leaving open whether reported gains reflect algorithmic improvements or evaluation artifacts. We present CaRE, a compute-aware evaluation framework that audits MDLM remasking strategies by standardizing actual number of function evaluations (NFE), enforcing multi-metric reporting, and explicitly controlling stochasticity. Applied to 7 remasking strategies across LLaDA-8B-Base and Dream-7B-Base at 4 stochasticity levels and 3 step budgets on OpenWebText and LM1B, CaRE reveals that: (i) temperature explains the majority of MAUVE variance, (ii) compute-matched comparisons reverse several published strategy rankings, and (iii) informed remasking and stochastic unmasking are in tension, with high-entropy remasking reducing MAUVE by 0.296 at 256 steps at unmask_temp=0.25 (p=0.020). A CaRE leaderboard covering 12 open-weight MDLMs (150M to 8B parameters) shows that this interaction direction holds across architectures and scales. These findings demonstrate that current MDLM evaluations can systematically conflate algorithmic improvements with hidden choices of compute and stochasticity. We release the evaluation protocol, implementation, and leaderboard to ensure future remasking claims are reproducible and comparable.

cs.AI↗
arXiv2026

Design and development of Fabry-Perot based wavelength calibration system for PARAS-2 spectrograph

Precise wavelength calibration is essential for high-precision radial velocity (RV) spectrographs, necessitating a stable calibrator that provides a dense grid of uniformly spaced lines to accurately determine stellar line positions and monitor instrumental drifts. In this work, we present the development of a cost-effective Fabry-Perot (FP) etalon-based wavelength calibrator designed to overcome the limitations of conventional sources such as hollow cathode lamps (HCLs) and iodine cells. This FP calibrator, combined with a Xenon (Xe) arc lamp assembly, has been integrated with the PARAS-2 spectrograph on the PRL 2.5m telescope at Mount Abu Observatory. Operated under controlled temperature and pressure conditions, the system generates a dense, comb-like spectrum covering 62 echelle orders with more than 10,000 well-defined and stable spectral lines, enabling precise measurement of instrumental drift. Initial results show that the free spectral range (FSR) varies from 0.16 Å~near 4000 Å~to 0.49 Å~ near 7000 Å, with a value of 0.3 Å~around the central wavelength of 5500 Å~. The estimated finesse ranges from 9 near 4000 Å~to 19 near 6900 Å, with an approximate value of 17 at 5500 Å. The temperature and pressure stability tests demonstrate RMS variations of $0.002 ^\circ\mathrm{C}$ and $5\times10^{-4}$ mbar, respectively. Based on these values, the theoretical stability of the FP wavelength calibrator is estimated to be within 10 cm/s, establishing it as a reliable alternative to Laser Frequency Combs (LFCs) for high-resolution spectroscopic calibration. We present an initial assessment of the RV stability of the FP calibrator, yielding 40-70 cm/s of relative drifts, which are up for further investigations. The observed excess over the theoretically estimated limit is likely attributable to instabilities arising from arc wandering in the xenon arc lamp.

astro-ph.IM↗
arXiv2026

TIMEGATE: Sustainable Time-Boxed Promotion Gates for Continual ML Adaptation Under Resource Constraints

As machine learning(ML) systems evolve to continual adaptation, each re-training cycle uses compute, annotation, and energy. We introduce TIMEGATE, a policy layer managing adaptation by budgeting time, labeling, training, and evaluation. TIMEGATE emits a metric-availability signal M for partial vs. full-evaluation decisions. We validate: (i) labeling outperforms training by 2.3x on Adult tabular; (ii) it transfers to LLaMA-3.1-8B + QLoRA on SST-2 (accuracy 0.80 to 0.96; M =1 in 35/36 runs); (iii) M is informative, 28-cell sensitivity shows M drops to 0.81 at tight thresholds; (iv) 100-cycle simulation achieves 66% evaluation-compute savings with no silent mis-promotions; (v) 10%-slice evaluation on LLaMA uses 89% less wall-clock and energy on a single H200 (ratios agree to 0.2%).

cs.LG↗
arXiv2026

TOI-7154b: A Close-in Massive Brown Dwarf in an Eccentric Orbit

We report here the discovery and characterization of a high-mass transiting brown dwarf in a close-in orbit around its host star, TOI-7154. Initially, the host star was identified as an exoplanetary candidate from the TESS photometry data. Later, with the mass measurements from the RV follow-up using the PARAS-2 and TRES spectrographs, the companion is found to be sub-stellar in nature. TOI-7154, is a G-type main-sequence metal-rich star metallicity $\mathrm{[Fe/H]} = 0.154^{+0.077}_{-0.075}\,\text{dex}$, effective temperature $T_{\mathrm{eff}} = 5564^{+100}_{-110}\,\text{K}$, mass $M_\star = 0.939^{+0.047}_{-0.043}\,M_{\odot}$, radius $R_\star = 0.949^{+0.032}_{-0.030}\,R_{\odot}$, and surface gravity $\log g = 4.456^{+0.036}_{-0.036}$. With the joint analysis of the TESS photometry and the PARAS-2 and TRES radial velocities we found that TOI-7154b orbits its host star in $P = 8.860073\pm 0.000029\,\text{d}$, eccentric ($e = 0.2482 \pm 0.0024$) orbit and its radius is smaller than that of Jupiter ($R_{b} = 0.827^{+0.040}_{-0.037}\,R_{\mathrm{J}}$). With a mass near the hydrogen-burning boundary ($M_{b} = 71.7^{+2.4}_{-2.2}\,M_{\mathrm{J}}$) which separates brown dwarfs from very low-mass stars, TOI-7154b occupies a critical position in the regime for probing the transition between sub-stellar and stellar objects. The system is very old, with its age estimated to be $7.2^{+3.9}_{-3.6}\,\text{Gyr}$ by MIST isochrones, while Galactic kinematics indicate an age of $\sim4-5\,\text{Gyr}$. {Our tidal evolution simulations indicate a stellar dissipation factor of $Q_\star'\lesssim10^6$. Since the presence of any companion is currently ruled out by observations, the presence of eccentricity in this old system is, therefore, indicative of it having stellar-like fragmentation origins.

astro-ph.EP↗
arXiv2026

OSCAR: Orchestrated Self-verification and Cross-path Refinement

Diffusion language models (DLMs) expose their denoising trajectories, offering a natural handle for inference-time control; accordingly, an ideal hallucination mitigation framework should intervene during generation using this model-native signal rather than relying on an externally trained hallucination classifier. Toward this, we formulate commitment uncertainty localization: given a denoising trajectory, identify token positions whose cross-chain entropy exceeds an unsupervised threshold before factually unreliable commitments propagate into self-consistent but incorrect outputs. We introduce a suite of trajectory-level assessments, including a cross-chain divergence-at-hallucination (CDH) metric, for principled comparison of localization methods. We also introduce OSCAR, a training-free inference-time framework operationalizing this formulation. OSCAR runs N parallel denoising chains with randomized reveal orders, computes cross-chain Shannon entropy to detect high-uncertainty positions, and then performs targeted remasking conditioned on retrieved evidence. Ablations confirm that localization and correction contribute complementary gains, robust across N in {4, 8, 16}. On TriviaQA, HotpotQA, RAGTruth, and CommonsenseQA using LLaDA-8B and Dream-7B, OSCAR enhances generation quality by significantly reducing hallucinated content and improving factual accuracy through uncertainty-guided remasking, which also facilitates more effective integration of retrieved evidence. Its native entropy-based uncertainty signal surpasses that of specialized trained detectors, highlighting an inherent capacity of diffusion language models to identify factual uncertainty that is not present in the sequential token commitment structure of autoregressive models.

cs.AI↗
arXiv2026

Economic complexity and regional development in India: Insights from a state-industry bipartite network

This study investigates the economic complexity of Indian states by constructing a state-industry bipartite network using firm-level data on registered companies and their paid-up capital. We compute the Economic Complexity Index and apply the fitness-complexity algorithm to quantify the diversity and sophistication of productive capabilities across the Indian states and two union territories. The results reveal substantial heterogeneity in regional capability structures, with states such as Maharashtra, Karnataka, and Delhi exhibiting consistently high complexity, while others remain concentrated in ubiquitous, low-value industries. The analysis also shows a strong positive relationship between complexity metrics and per-capita Gross State Domestic Product, underscoring the role of capability accumulation in shaping economic performance. Additionally, the number of active firms in India demonstrates a persistent exponential growth at an annual rate of 11.2%, reflecting ongoing formalization and industrial expansion. The ordered binary matrix displays the characteristic triangular structure observed in complexity studies, validating the applicability of complexity frameworks at the sub-national level. This work highlights the usefulness of firm-based data for assessing regional productive structures and emphasizes the importance of capability-oriented strategies for fostering balanced and sustainable development across Indian states. By demonstrating the usefulness of firm registry data in data constrained environments, this study advances the empirical application of economic complexity methods and provides a quantitative foundation for capability-oriented industrial and regional policy in India.

econ.GN↗
arXiv2025

The phase diagram of quantum chromodynamics in one dimension on a quantum computer

The quantum chromodynamics (QCD) phase diagram, which reveals the state of strongly interacting matter at different temperatures and densities, is key to answering open questions in physics, ranging from the behavior of particles in neutron stars to the conditions of the early universe. However, classical simulations of QCD face significant computational barriers, such as the sign problem at finite matter densities. Quantum computing offers a promising solution to overcome these challenges. Here, we take an important step toward exploring the QCD phase diagram with quantum devices by preparing thermal states in one-dimensional non-Abelian gauge theories. We experimentally simulate the thermal states of SU(2) and SU(3) gauge theories at finite densities on a trapped-ion quantum computer using a variational method. This is achieved by introducing two features: Firstly, we add motional ancillae to the existing qubit register to efficiently prepare thermal probability distributions. Secondly, we introduce charge-singlet measurements to enforce color-neutrality constraints. This work marks the first lattice gauge theory quantum simulation of QCD at finite density and temperature for two and three colors, laying the foundation to explore QCD phenomena on quantum platforms.

quant-ph↗
arXiv2025

The charge-singlet measurement toolbox

Symmetry is fundamental to physical laws across different scales$\unicode{x2014}$from spacetime structure in general relativity to particle interactions in quantum field theory. Local symmetries, described by gauge theories, are central to phenomena such as superconductivity, topological phases, and the Standard Model of particle physics. Emerging simulation techniques using tensor network states or quantum computers offer exciting new possibilities of exploring the physics of these gauge theories, but require careful implementation of gauge symmetry and charge-neutrality constraints. This is especially challenging for non-Abelian gauge theories such as quantum chromodynamics (QCD), which governs the strong interaction between quarks and gluons. In a recent article (arXiv:2501.00579), we introduced "charge-singlet measurements" for quantum simulations, consisting of a projection based technique from group representation theory that allowed us to probe for the first time the phase diagram of (1+1)-dimensional QCD on a quantum computer. In this article, we show more broadly how to apply charge-singlet measurements as a flexible tool for both classical and quantum simulations of discrete and continuous gauge theories. Our approach extends the use of charge-singlet measurements beyond state preparation in the charge neutral (charge-singlet) sector to include noise mitigation in symmetry-preserving time-evolution circuits. We further demonstrate how this method enables the computation of thermodynamic observables$\unicode{x2014}$such as entropy$\unicode{x2014}$within the charge-singlet subspace, providing a new tool for exploring the connection between quantum thermodynamics and gauge symmetry.

quant-ph↗