arXiv subjects

Shashank Srivastava

Publications and source records attributed to Shashank Srivastava.

At least 19 recordsLinked to original sources

arXiv2026

Recalibration-Aware Optimization of ZEFOZ Operating Points in $^{167}\mathrm{Er}^{3+}:\mathrm{Y}_2\mathrm{SiO}_5$ for Quantum Memories

Erbium-doped solids like $^{167}\mathrm{Er}^{3+}:\mathrm{Y}_2\mathrm{SiO}_5$ have properties that make them suitable for quantum-network memories, however, their unquenched effective electronic spin causes rapid magnetic decoherence. Operating at Zero First-Order Zeeman (ZEFOZ) points allows us to mitigate this, where we evaluate the ZEFOZ landscape for Site-2 $^{167}\mathrm{Er}^{3+}$ by incorporating a full-Hessian spin-Hamiltonian treatment with the anisotropic magnetic-field covariance derived from the native $^{89}\mathrm{Y}$ crystal lattice directly. Operating-point screening criteria were adopted to devise a Pareto-optimal frontier of four transition pairs to balance modeled Y-only magnetic pure-dephasing lifetimes ($T_{ϕ,Y}^{(2)}$) against microwave control authority. The $ε_{14}\leftrightarrowε_{15}$ transition exhibits the longest modeled Y-bath-induced quadratic pure-dephasing time, $T_{ϕ,Y}^{(2)}>2.2\times10^5~\mathrm{s}$, while the $ε_{12}\leftrightarrowε_{13}$ transition stood out, achieving a Rabi frequency coefficient of $1.248 \times 10^5~\mathrm{Hz/mT}$. An assumption-based Hamiltonian-uncertainty analysis demonstrates that while uncompensated deviations shift the ZEFOZ condition away from its nominal magnetic-field coordinate, the shifted operating points can be recovered via numerical vector-field recalibration. The Pareto-optimal coordinates were mathematically recovered for all sampled Hamiltonian perturbations, and the $84.1^{\mathrm{st}}$-percentile field relocations ranged from $10.95$ to $27.97~\mathrm{mT}$. The intended operation near $10~\mathrm{mK}$ must be taken into account for the suppression of Er-induced spectral diffusion through electronic-spin polarization, while the extended Y-bath pure-dephasing timescales also remain conditional on the adopted temporal-correlation model.

quant-ph↗

arXiv2026

Language Models Show a "Women-Are-Wonderful" Effect in Blame Attribution That is Not Just Leniency

This paper investigates the Women-Are-Wonderful (WaW) effect in language-model blame attribution: a favorable bias in which women receive more positive evaluations than men. Using 11,270 interpersonal conflicts from r/AmItheAsshole, we construct matched counterfactuals that vary the gender of the person being judged (the original poster) and the person affected (the counter-party). Across four language models, gender changes a minority of verdicts, but those changes consistently favor women. The effect is strongly role-dependent. Making the original poster a woman reduces the odds of blame by 9--52%, while making the counter-party a woman multiplies them by 2.7--9.2X. Counter-party WaW also varies across conflict types, strengthening in communication and relationship conflicts and weakening in family conflicts. Exploratory probes suggest that counter-party gender can shift judgments of sympathy, reasonableness, harm and protection. WaW is therefore not just leniency toward women being judged: models also judge conduct more harshly when it affects a woman.

cs.CL↗

arXiv2026

AdaTutor: Adaptive Tutoring through Pedagogical Strategy Selection

Effective tutoring requires determining not only what information to provide, but also which form of assistance to deliver. A tutor may address the same student error using various pedagogical strategies, adapting its approach as the interaction progresses. We introduce Comparative Pedagogical Supervision (CPS), a training framework to learn to select the optimal strategy and feedback for student learning. We evaluate CPS on math and science reasoning datasets. After five tutoring turns, CPS outperforms the strongest fixed-strategy baselines. The learned tutor transfers across datasets, and its strategy selection can also inform a separate API-based tutor.

cs.AI↗

arXiv2026

Random Quantum LDPC Codes Approaching the Gilbert-Varshamov Bound

We show that for any $R,ε>0$ and any prime $p\geq 2$, there exists an infinite family of $p$-ary quantum low-density parity-check (QLDPC) codes, rate $R$, checks of weight $O_ε(1)$, and normalized distance at least $δ_{\mathrm{GV}}(p,R)-ε$. Here, $δ_{\mathrm{GV}}(p,R)$ denotes the quantum Gilbert-Varshamov (GV) bound for $p$-ary stabilizer codes. In fact, we construct an ensemble of such QLDPC codes, such that a random code from this ensemble is close to the GV bound with high probability. Moreover, this ensemble matches the performance of random stabilizer codes on several quantum channels. Specifically, it approaches the quantum capacity of the erasure channel and the hashing bound for memoryless Pauli channels, including the depolarizing channel. A significant challenge in working with QLDPC codes is that they are necessarily \emph{degenerate}, i.e., contain many low-weight stabilizers. A key contribution of our work is a construction of QLDPC codes with quantitative control on their degeneracy. These codes are obtained by combining known constructions of asymptotically good QLDPC codes with the expander-based distance amplification procedure of Alon, Edmonds, and Luby [FOCS'95]. Our random ensemble is constructed by starting with these low-degeneracy QLDPC codes near the quantum Singleton bound and concatenating each coordinate with a random inner code. This can be viewed as a quantum analogue of Thommesen's construction, and as an LDPC version of a result of Ouyang, with an appropriately designed outer code.

cs.IT↗

arXiv2026

UNMASK: Discovering and Causally Verifying Spurious Shortcuts in Text Classifiers

Neural language models trained on large crowdsourced corpora frequently exploit spurious surface patterns tied to target labels without true linguistic or causal relevance, boosting benchmark performance while failing on adversarial or out-of-distribution inputs. Existing approaches either require manual specification of the feature vocabulary or automate discovery only partially, leaving the gap between dataset-level correlation and model-level exploitation unaddressed. We present U N M ASK, a fully automated pipeline that discovers, causally verifies, and mitigates spurious correlations in text classifiers without additional human annotation. Given unlabeled training examples, U N M ASK generates candidate surface patterns as executable boolean expressions, filters them through a statistical validation protocol with independent replication, and establishes causal model dependence via verified counterfactual interventions. Causally confirmed features then serve as annotation-free group definitions for Deep Feature Reweighting, eliminating the group labels that standard DFR requires. Applied to BERT and RoBERTa trained on MNLI, our pipeline independently rediscovers established lexical-overlap and negation biases, verifying 9 of 10 features on BERT and 6 on RoBERTa, and improving HANS accuracy by up to 12.58 pp. On CivilComments-WILDS, programmatic groups match the 70.1% worst- group accuracy of hand-labeled DFR (Kirichenko et al., 2023) without demographic annotation. We further demonstrate that the discovery and validation stages generalize to reward model preference data, surfacing interpretable spurious correlations in RewardBench2.

cs.CL↗

arXiv2026

The Story Shapes the Agent: Narrative Priors in LLM Behavior

Persona prompting is widely used to steer LLM agent behavior, yet the narrative framing of a task can matter more than the assigned persona. We isolate this effect through structural isomorphism, constructing three text-based investigation games that share the same action space, stage progression, and resource constraints while varying only task narrative: disease investigation, IT troubleshooting, and murder mystery. Across 1,890 sessions spanning 3 models and 10 personas, we identify narrative priors: systematic action tendencies activated by a task's story framing, independent of its decision structure. Narrative priors explain 5-31x more behavioral variance than persona, are consistent across model architectures, and in two of three domains are negatively associated with task success. Persona effects that do transfer across narratives arise from behavioral anchors, persona descriptions whose language maps directly onto shared actions. Causal interventions confirm this: removing anchor words from a high-transfer persona reduces cross-narrative consistency by 95%. Our framework also generalizes to a held-out fourth narrative and yields a persona-selection method that improves cross-narrative transfer. These results suggest that LLM behavior that survives narrative changes should be grounded in concrete actions rather than abstract descriptions.

cs.CL↗

arXiv2026

Point of Order: Action-Aware LLM Persona Modeling for Data-Grounded Civic Deliberation

LLM-based simulations can enable controlled studies of civic deliberation, but current systems lack speaker-attributed data and methods for evaluating long-form institutional behavior. ASR transcripts typically use anonymous labels such as $Speaker\_1$, preventing models from learning stable participant behavior across meetings. We present a reproducible pipeline that converts public Zoom recordings into speaker-attributed transcripts enriched with persona profiles, topics, and pragmatic "action tags" such as $[propose\_motion]$. Using this pipeline, we release three public datasets of government deliberation (Appellate Court hearings, School Board meetings, and Municipal Council sessions) and fine-tune LLM personas on this action-aware data. We evaluate simulations along four dimensions: persona fidelity, persona consistency, institutional fidelity, and behavioral coherence. Action-aware fine-tuning cuts perplexity by 67%, doubles classifier-based persona fidelity, increases vote attempts by up to $3.6\times$, and improves deliberative responsiveness by up to 70%. Human evaluations show that simulated excerpts are often hard to distinguish from real deliberations, indicating a practical foundation for data-grounded civic simulation studies.

cs.CL↗

arXiv2026

Unconventional Superconductivity in the Chiral Topological Semimetal Ag2Pd3S

Chiral crystals provide a unique setting where broken inversion symmetry, strong spin-orbit coupling, and electronic topology intertwine, yet superconductivity in intrinsically chiral materials remains rare. Here, we report unconventional superconductivity in the chiral topological semimetal Ag$_2$Pd$_3$S, an enantiomorphic analog of natural mineral coldwellite, crystallizing in the right-handed space group $P4_132$. Bulk superconductivity with a transition temperature $T_C = 1.1(2)$ K is confirmed by electrical resistivity, magnetization, and specific-heat measurements. Muon spin rotation and relaxation ($μ$SR) experiments reveal a fully gapped superconducting state that spontaneously time-reversal symmetry (TRS) breaking establishing Ag$_2$Pd$_3$S as the first chiral topological semimetal superconductor exhibiting intrinsic TRS breaking. First-principles calculations uncover multiple multifold band crossings near the Fermi level, hosting Kramers-Weyl, double spin-1, and spin-3/2 quasiparticles with large topological charges. These unconventional fermions generate symmetry-protected topological surface states and underscore the nontrivial topology of the normal state. Symmetry analysis based on the Ginzburg-Landau theory suggests a loop-supercurrent-ordered superconducting state, yielding a full gap alongside spontaneous TRS breaking. The coexistence of TRS-breaking superconductivity and chiral multifold fermions identifies Ag$_2$Pd$_3$S as a platform for realizing intrinsic superconducting diode effects and chirality-induced spin selectivity, offering a transformative pathway toward dissipationless topological quantum technologies.

cond-mat.supr-con↗

arXiv2026

The Point of No Return: Counterfactual Localization of Deceptive Commitment in Language-Model Reasoning

Existing deception datasets label completed outputs as honest or deceptive, treating deception as a property of the final response rather than a function of the model's reasoning trace. This obscures a more fundamental question: when does a language model become committed to deception? We introduce counterfactual localization: for each sentence prefix in a reasoning trace, we fix the prefix, resample continuations, and estimate the probability of a deceptive outcome. To scale this, we construct five environments (spanning strategic bluffing, maze guidance, financial advice, used-car sales, and offer negotiation) in which deception is never prompted but emerges from strategic incentives and labels follow mechanically from environment state rather than subjective human judgment. The resulting corpus localizes $\sim$1.46M sentences across four reasoning models, drawn from over 94.1M sampled continuations, 91.5B generated tokens, and over 100K scenarios. Sentence-level human evaluation confirms that detected commitment points correspond to interpretable shifts in decision state. Using this resource, we show that lexical cues for commitment prediction transfer poorly across environments, whereas attention-based transition features generalize out of distribution, suggesting that deceptive commitment is reflected in reusable changes in reasoning dynamics rather than surface form. We further identify compact attention-head sets (under 10% of heads) that, selected on one environment, causally suppress deceptive commitment across held-out environments. We release the corpus as a substrate for studying deception, and more broadly commitment, in language-model reasoning.

cs.CL↗

arXiv2026

Is Chain-of-Thought Really Not Explainability? Chain-of-Thought Can Be Faithful without Hint Verbalization

Recent work, using the Biasing Features metric, labels a CoT as unfaithful if it omits a prompt-injected hint that affected the prediction. We argue this metric adopts a narrow notion of faithfulness and confuses unfaithfulness with incompleteness, the lossy compression needed to turn distributed transformer computation into a linear natural language narrative. On multi-hop reasoning tasks with instruct-tuned and reasoning models, many CoTs flagged as unfaithful by Biasing Features are judged faithful by other metrics, exceeding 50% in some models. With a new faithful@k metric, we show that larger inference-time budgets greatly increase hint verbalization (up to 90% in some settings), suggesting much apparent unfaithfulness is due to tight token limits. Using Causal Mediation Analysis, we further show that even non-verbalized hints can causally mediate prediction changes through the CoT. We therefore caution against relying solely on hint-based evaluations and advocate a broader interpretability toolkit, including causal mediation and corruption-based metrics. We do not claim all CoTs are faithful, only that the absence of hint words alone does not prove unfaithfulness.

cs.CL↗

arXiv2026

Discovery of Quasi One Dimensional Superconductivity in PtPb3Bi

Quasi one dimensional materials provide a compelling platform where reduced dimensionality stabilizes intertwined topological and superconducting phases. Here we report superconductivity in a new Bi based quasi 1D compound, PtPb3Bi, which hosts a nontrivial electronic structure. It exhibits type II superconductivity below 3.01(1) K. Heat capacity and transverse field muon spin rotation relaxation (muSR) measurements demonstrate a fully gapped isotropic s wave state with moderate electron phonon coupling, while zero field muSR confirms the preservation of time reversal symmetry (TRS). Transport measurements reveal low carrier mobility with diffusive normal state transport. Electronic structure calculations show strong dispersion along the quasi 1D direction and relatively flatter bands in the transverse plane, giving rise to pronounced Fermi surface nesting in the kx-ky plane. Consistent with this, the compound undergoes a charge density wave transition at 280(1) K. The flow of Wannier charge centers, together with surface state dispersion, establishes nontrivial band topology. These results identify PtPb3Bi as a new quasi 1D superconductor with nontrivial electronic structure and a promising candidate for topological superconductivity.

cond-mat.supr-con↗

arXiv2026

Hourglass Dirac chains enable intrinsic topological superconductivity in nonsymmorphic silicides

Nonsymmorphic crystalline symmetries provide a robust route to symmetry-protected electronic topology, yet their role in stabilizing intrinsic topological superconductivity remains largely unexplored. Here, we report \ch{TaPtSi} as a new member of the superconducting nonsymmorphic silicide family, characterized via AC transport, magnetization, heat capacity, and muon spin rotation/relaxation ($μ$SR) measurements. Zero field $μ$SR reveals spontaneous internal magnetic fields below $T_{\rm c}$, establishing time reversal symmetry breaking in \ch{TaPtSi}. First principles calculations on \ch{TaPtSi} and its isostructural nonsymmorphic superconducting analogues reveal the presence of symmetry-protected hourglass dispersions. The "necks" of these dispersions form Dirac nodal rings and chains that reside near or intersect the Fermi level. Guided by Ginzburg Landau symmetry analysis, we identify an internally antisymmetric non unitary triplet pairing state as the unique ground state consistent with the experimental phenomenology. Based on Bogoliubov de Gennes calculations, we further demonstrate that this state supports Majorana surface modes, establishing its intrinsically topological nature. These results reveal a systematic route by which nonsymmorphic symmetry drives the interplay between hourglass Dirac chain topology and unconventional triplet pairing, positioning equiatomic silicides as a unified materials platform for intrinsic topological superconductivity.

cond-mat.supr-con↗

arXiv2026

Towards Universal Neural Likelihood Inference

We introduce universal neural likelihood inference (UNLI): enabling a single model to provide data-grounded, conditional likelihood predictions for arbitrary targets given any collection of observed features, across diverse domains and tasks. To achieve UNLI over heterogeneous tabular data, we develop the Arbitrary Set-based Permutation-Invariant Reasoning Engine (ASPIRE) model. Our design addresses critical gaps in existing approaches to merge semantic-understanding capabilities and generalised numerical feature reasoning within a zero-shot capable framework. Trained on over 1,400 real diverse datasets spanning various domains, ASPIRE achieves 15\% higher F1 scores and 85\% lower RMSE than existing tabular foundation models in zero-shot and few-shot settings. Lastly, this work introduces open-world active feature acquisition, where we leverage the UNLI capabilities of ASPIRE to adeptly determine next feature-values to observe to improve inference time prediction accuracies.

cs.LG↗

arXiv2026

DiffVax: Optimization-Free Image Immunization Against Diffusion-Based Editing

Current image immunization defense techniques against diffusion-based editing embed imperceptible noise into target images to disrupt editing models. However, these methods face scalability challenges, as they require time-consuming optimization for each image separately, taking hours for small batches. To address these challenges, we introduce DiffVax, a scalable, lightweight, and optimization-free framework for image immunization, specifically designed to prevent diffusion-based editing. Our approach enables effective generalization to unseen content, reducing computational costs and cutting immunization time from days to milliseconds, achieving a speedup of 250,000x. This is achieved through a loss term that ensures the failure of editing attempts and the imperceptibility of the perturbations. Extensive qualitative and quantitative results demonstrate that our model is scalable, optimization-free, adaptable to various diffusion-based editing tools, robust against counter-attacks, and, for the first time, effectively protects video content from editing. More details are available in https://diffvax.github.io/ .

cs.CV↗

arXiv2026

Observation of Time-Reversal Symmetry Breaking in the Type-I Superconductor YbSb$_2$

The spontaneous breaking of time-reversal symmetry is a hallmark of unconventional superconductivity, typically observed in type-II superconductors. Here, we report evidence of time-reversal symmetry breaking in the type-I superconductor YbSb$_2$. Zero-field $μ$SR measurements reveal spontaneous internal magnetic fields emerging just below the superconducting transition, while transverse-field $μ$SR confirms a fully gapped type-I superconducting state. Our first-principles calculations identify YbSb$_2$ as a ${\mathbb Z}_2$ topological metal hosting a Dirac nodal line near the Fermi level. Symmetry analysis within the Ginzburg Landau framework indicates an internally antisymmetric nonunitary triplet (INT) state as the most probable superconducting ground state. Calculations based on an effective low-energy model further demonstrate that this INT state hosts gapless Majorana surface modes, establishing YbSb$_2$ as a topological superconductor. Our results highlight YbSb$_2$ as a unique material platform where type-I superconductivity coexists with triplet-pairing and nontrivial topology.

cond-mat.supr-con↗

arXiv2026

Socratic Students: Teaching Language Models to Learn by Asking Questions

Large language Models (LLMs) are usually used to answer questions, but many high-stakes applications (e.g., tutoring, clinical support) require the complementary skill of asking questions: detecting missing information, requesting clarifications, and using them to solve tasks. We study this skill in reasoning-heavy domains where progress depends on inquiry rather than factual recall. We define an interactive protocol where a student model engages a stronger teacher under a small turn budget. After each teacher reply, we evaluate the student on the original task with Pass@k. We propose Outcome-Driven Question optimization Strategy (ODQS ), a training framework that learns a questioning policy from downstream task outcomes. At each turn, we sample multiple candidate questions; query the teacher with each, then score the student's resulting performance. Using these scores, we train the student via supervised fine-tuning followed by Direct Preference Optimization (DPO), without any human labels. On GSM8K, HumanEval, and OpenCoder, ODQS produces large gains over interactive baselines, boosting Pass@5 by up to 54.7% (absolute) on math and 22.9% (absolute) on coding, and matching baseline performance in three fewer turns. Thus, question asking can be explicitly trained from task outcomes, improving both accuracy and efficiency in interactive reasoning.

cs.AI↗

arXiv2025

A Causal Lens for Evaluating Faithfulness Metrics

Large Language Models (LLMs) offer natural language explanations as an alternative to feature attribution methods for model interpretability. However, despite their plausibility, they may not reflect the model's true reasoning faithfully. While several faithfulness metrics have been proposed, they are often evaluated in isolation, making principled comparisons between them difficult. We present Causal Diagnosticity, a testbed framework for evaluating faithfulness metrics for natural language explanations. We use the concept of diagnosticity, and employ model-editing methods to generate faithful-unfaithful explanation pairs. Our benchmark includes four tasks: fact-checking, analogy, object counting, and multi-hop reasoning. We evaluate prominent faithfulness metrics, including post-hoc explanation and chain-of-thought methods. Diagnostic performance varies across tasks and models, with Filler Tokens performing best overall. Additionally, continuous metrics are generally more diagnostic than binary ones but can be sensitive to noise and model choice. Our results highlight the need for more robust faithfulness metrics.

cs.CL↗

arXiv2025

Probing the intermediate state of type-I superconductor SnAs using Muon Spin Spectroscopy

Superconductivity with non-trivial band topology provides a novel platform for exploring topological superconductivity and its quantum applications. A detailed microscopic understanding of the superconducting ground state in such materials is crucial. Here, we report the results of a muon spin rotation/relaxation study ($μ$SR) of the topologically non-trivial superconductor SnAs, which exhibits superconductivity below 3.74(1) \si{K}. Zero-field (ZF) $μ$SR data reveal that this system is a time-reversal invariant superconductor, and systematic transverse-field (TF) $μ$SR measurements unveil the type-I nature of the SnAs superconductor. We have established the superconducting phase diagram to understand the intermediate state of type-I superconductors. Moreover, ab \textit{initio} band structure and phonon calculations are performed, which correlate with the experimental characterization.

cond-mat.supr-con↗