Abstract

We study the problem of selecting large language models (LLMs) for user queries in settings where multiple LLM providers submit the cost of solving a query. From the user's perspective, choosing the optimal model is a sequential, query-dependent decision problem: high-capacity models offer more reliable outputs but are costlier, while lightweight models are faster and cheaper. We formalize this interaction as a reverse auction mechanism design problem with contextual online learning, where the user adaptively discovers which model performs best while eliciting costs from competing LLM providers. Existing multi-armed bandit (MAB) mechanisms focus on forward auctions and social welfare, leaving open the challenges of reverse auctions, provider-optimal outcomes, and contextual adaptation. We address these gaps by designing a resampling-based procedure that generalizes truthful forward MAB mechanisms to reverse auctions and prove that any monotone allocation rule with this procedure is truthful. Using this, we propose a contextual MAB algorithm that learns query-dependent model quality with sublinear regret. Our framework unifies mechanism design and adaptive learning, enabling efficient, truthful, and query-aware LLM selection.

Abstract

Standard depth sensors systematically fail on transparent surfaces, creating corrupted 3D maps and severe navigation hazards. While specialized hardware sensors can detect glass, they lack modularity and have extensive hardware dependencies. Consequently, learning-based monocular depth estimation has emerged as a compelling alternative. However, domain-specific glass-aware monocular depth estimators struggle with unfamiliar indoor layouts; restricted by the severe scarcity of real-world glass depth annotations, they fail to generalize zero-shot to new settings. This motivates us to explore whether the extensive priors of text-to-image diffusion models can enable generalizable perception of transparent surfaces. We introduce SILICA, a unified pipeline leveraging these priors to jointly predict glass segmentation and glass-aware depth. This mutual information exchange establishes a robust spatial hierarchy, entirely eliminating the need for paired real-world glass depth annotations. Subsequently, we use the predicted segmentation mask to explicitly filter incorrect glass depth points from standard sensors, recovering accurate metric glass depth for downstream 3D mapping and autonomous collision avoidance. Supported by our novel Mirage 18k dataset, extensive experiments demonstrate that SILICA achieves remarkable zero-shot transfer across diverse, unseen environments, outperforming state-of-the-art models by almost 20% and setting a new benchmark for transparent surface perception.

Abstract

How do humans allocate scarce cognitive resources across a stream of related decisions? Russek et al. (2025) predict reaction time on each chess move from the within‑move Benefit of Computation, treating moves as if they were drawn independently. We ask whether those costs also depend on computation carried forward from the previous move. In chess, a player who has calculated a likely opponent reply can often reuse that work; a surprising reply should force revision. On 135,608 live‑game tactical sequences (extracted from the Lichess puzzle database and re‑timed against the original 2019 Lichess game database) played by 74,609 unique players, the within‑sequence log‑RT spike is +0.309 (Cohen's 𝑑 = 0.235; paired 𝑡 = 86, N = 135,608; BF10 > 10100). Hierarchical regression climbs from 𝑅² = 0.011 (Russek‑static) to 0.282; the amortization interaction is large when predictability is operationalized by the human‑style Maia‑2 (𝛽 = −0.054, 𝑝 < 10⁻²⁷) and not significant under Stockfish best‑move predictability. A three‑parameter Bayesian Sampling Model with Cache recovers cache strength 𝑠 = 0.085 [0.071, 0.099], with 100% of bootstrap draws positive. The H1 spike is roughly twice as large in solved sequences as in failed ones (𝑡 = 26.2, 𝑝 < 10⁻¹⁵⁰); this pattern suggests successful tactical recognition followed by decisive recalculation rather than passive surprise. The findings replicate on a 2024 Lichess cohort and on FIDE World Rapid + Blitz 2024 over‑the‑board play.

Abstract

We investigate whether scaling model parameters improves humor generation through a controlled ablation study. Using five Qwen3 variants (8B-235B, dense and MoE), we generate jokes across 50 themes. Beyond evaluating humor scaling, this work serves as an empirical study into the nature of LLM versus human evaluations on highly subjective creative tasks. While an automated judge yields a perfect monotonic ranking between parameter count and win rate, human annotators find no significant aggregate difference in humor quality. Restricting to themes where annotators agree reveals a significant preference for the largest model (p = 0.039), suggesting scaling effects exist but are masked by a "quality floor." Crucially, our analysis of bias characteristics shows that the automated judge exhibits severe positional and length biases compared to human evaluators, further suggesting that LLMs may systematically distort quality differences on subjective tasks.

Abstract

We present a neurosymbolic pipeline for computational humor generation grounded in the General Theory of Verbal Humor. The system constructs the joke in five sequential stages: context analysis, humor architecture (identifying core incongruity), delivery strategy, content writing, and pairwise judging, orchestrated through the DSPy framework. The system generates four candidate jokes per input with independent humor strategies, then selects the best through knockout tournament-style evaluation. Despite using Gemma 3 27B, a model with roughly 20× fewer total parameters than frontier systems, our approach achieves competitive results across all five subtasks of SemEval- 2026 Task 1 (MWAHAHA), placing 2nd in two subtasks. We argue that these results demonstrate the viability of structured, theory-driven decomposition for solving complex tasks and that how a model reasons about humor is just as important as how large the model is.

Abstract

Introduction According to the industry' latest definitive benchmark, the "India Influencer Marketing Report 2025," released by WPP's The Goat Agency and Kantar in June 2025, the sector was valued at |3,600 crore in 2024 and is projected to grow by 25% in 2025. For content creators, this financial influx signifies a transition from casual social presence to a professionalized economy where earnings are derived from online content, ranging from fixed-fee brand endorsements and affiliate marketing to long-term ambassadorships. While influencers have been widely researched globally, there are limited studies on the Indian influencer economy. Recent studies that do cover them primarily focus on topics such as their cultural impact, consumer culture, trust between brands and customers, para-social relationships, and the ethical risks of being an influencer. The global scholarship on influencers has also examined how inequalities across race, gender, social and socioeconomic status, and sexual orientation shape the influencer economy. Another strand of scholarship investigates monetary and non-monetary transactions, such as gifts, free products, discount coupons, or gift cards, within the influencer economy (Duffy, 2017; Christin and Lu, 2023). The payment amount is highly context-dependent, making it vague and open to interpretation. This ambiguity, Carbone (2019) argues, opens the door to different forms of discrimination. However, there is limited scholarly attention to how monetary and non-monetary compensation is negotiated in practice. This gap also indicates the presence of diverse spheres of circulation within the influencer economy. We use the anthropology of barter exchange as a framework to investigate the economic circuits in which early-career influencers engage. Treating the influencer economy as having distinct transactional cultures, we envisage interpreting value creation and circulation within local contexts. Recent studies on barter demonstrate that it is not a remnant of the old order but is present across various demographics (Stapleton, 2022). Borrowing from Gudeman (2001), we interpret the initial value circulation in the influencers' lifecycle through a lens of 'self-currency', i.e., the non-monetary exchanges a participant in this economy requires to be engaged. Thus, rather than a monetary valuation, the commodity itself is exchanged for its inherent value. Further, Gudeman (2001) argues that in the contemporary economy, there exists a trader's dilemma: should one try to increase their profits or maintain their relations? Thus, this investigation centers the influencer economy in India to inquire into the subterranean circulation of values and their logics. Objective/Research Question To investigate the above scholarly gap, we ask: How does barter as an exchange logic appear in the influencer economy? Methods In the influencer industry, as Trevisan & Farinosi (2024) argue, fashion influencers are among the most successful. Influencer marketing has the largest reach in the fashion, beauty, and lifestyle segments. Thus, we focus on fashion micro-influencers in our study, as research suggests that marketers are shifting away from mega and macro influencers toward micro-influencers, who appear more relatable and thus able to attract more commercial prospects (Britt et al., 2020). For this investigation, the third author conducted an interpretivist ethnographic enquiry. The methods used in the field were content analysis and semi-structured interviews. 10 female Instagram micro-influencers were selected and interviewed from Kolkata, India. In addition, we interviewed 2 brand owners (both females). All of them engaged in the lifestyle and clothing sectors. The influencers were selected through a snowball sampling method. In the definition of micro-influencers, influencers have a following between 10,000 to 100,000. All the microinfluencers that we interviewed and whose Instagrams we studied fell within this category. The interviews and their social media actions demonstrated the high frequency of collaborations between them and various other stakeholders (for instance, friends and brands). Following this thread, we investigate the spheres of circulation as a social phenomenon during various stages of influencers' growth. The collected data were analyzed through thematic interpretation. Some of the major themes that emerged are: social ties in content production, commodity exchange, relatability, and network size. For ethical purposes, all identity markers have been anonymized. Results Our interview findings indicate three distinct phases of growth for influencers. First, the altruistic phase, where micro-influencers create content with acquaintances. The second stage is the commodity barter phase, where the influencers create content for brands and boutiques in exchange for a commodity. The third phase is the monetisation phase, which involves producing content for brands in exchange for money. Both the altruistic phase and the commodity barter phase are embedded in the logic of barter. In the altruistic phase, aspiring influencers form friendships to collaborate with aspiring photographers, editors, and beauty professionals to create content. The idea being that this free labor, donated by everyone, will lead to the growth of all involved. In terms of Instagram growth, this would translate into more followers and, in turn, an expansive fan base. Apart from their labour, micro-influencers create relatable content. This 'relatability' means that the community of followers resonates and approves of the content created; in addition, the community feels that the micro-influencer is similar to them in terms of social position and life choices. In platform terminology, the altruistic phase is key to achieving network effects and building a community. This phase is followed by the commodity barter phase, in which either the micro-influencer or the brands can reach out to the other party to collaborate. Our respondents stated that they created content while wearing the brands' products (mostly clothes) and, in return, kept the products themselves. If there are multiple commodities, they are still paid a single remuneration. In such barter collaborations, brands bear the production costs (for instance, hiring professional photographers). Money is not considered an exchange tool in such barter collaborations, and it is not discussed. Until the micro-influencer has reached a minimum number of followers (close to 50k) and is working on multiple barter collaborations, they are not offered collaborations with monetary compensation. The key ingredient is the exchange of one's followership (network size) for the commodity (for instance, a dress) in the commodity barter phase. Whereas in the altruistic phase, there is an exchange of labour to increase the network size for all participants. The findings consist of the anthropologies of exchange, where diverse spheres of circulation exist, and in the digital world, it is not exclusively impregnated by monetary logics. In a similar line to the extant literature on anthropology of barter, there is the exchange of self-currency the network size becomes a non-monetary commodity being exchanged. Just as in the trader's dilemma, our respondents want to achieve monetary gains through content production, yet they are aware of the social ties (here, the community of followers). Thus, micro-influencers carefully choose only those collaborations that will be appreciated by their followers. Future Work Building on these findings, we envisage investigating the monetization phase of micro-influencers. In addition to the above research interest, we are looking at the aesthetic performativity of fashion micro-influencers. Thus, we ask, How do micro-influencers negotiate authenticity in a competitive social media landscape in India.

Abstract

Abstract--An integrated framework for non-contact respiratory
rate (RR) estimate using a Zynq-7000 SoC platform and
a 77 GHz FMCW radar is presented in this work. For shortrange
respiratory monitoring, three lightweight signal processing
techniques multitaper spectrum analysis (MSA), adaptive harmonic
component analysis (AHCA), and autoregressive linear
predictive coding (AR-LPC) are compared. AR-LPC achieves
the lowest estimation error among the evaluated approaches,
according to an experimental evaluation conducted in controlled
indoor settings, whereas MSA offers stable performance across a
range of distances. The viability of implementing computationally
light radar-based respiratory monitoring algorithms on portable
edge platforms is demonstrated by the suggested implementation.
In order to direct future development toward robust deployment,
limitations related to subject diversity, recording time, and
motion conditions are also explored.

Abstract

Tool-using LLM agents fail in ways that are poorly captured by atomic safety tests. In indirect prompt injection, an untrusted message, a private database, and an outbound tool may each be useful in isolation, yet their composition enables exfiltration or unauthorized action. We introduce CCL-Bench, a capability-lattice evaluation protocol that converts existing agent-security benchmarks into matched counterfactual variants. The protocol estimates higher-order safety interactions such as the superadditive risk of untrusted context, private data, and external sinks. We then propose CAPSPLIT-IB, an interaction-barrier shield that models unsafe capability compositions as weighted hyperedges and repairs unsafe tool calls through a constrained minimum-cost intervention. Across AgentDojo, InjecAgent, and held-out AgentDyn tasks, CAPSPLIT-IB reduces ASR from 27.03% (NoDefense) to 1.18% in the main aggregate, while maintaining low ODR among defended methods and improving context-dependent utility over flow-based baselines. These results support a compositional view of agent safety: useful capabilities can be safe alone but unsafe in combination.

Abstract

Visual Place Recognition (VPR) enables long-term localization by matching query images against a reference database, yet remains vulnerable to severe appearance changes, viewpoint shifts, and perceptual aliasing. We propose SegCapVPR, a unified framework for multimodal and cross-modal VPR that jointly models global scene context, segment-level structure, and aligned linguistic semantics. Our method extracts whole-image and region-level features using DINOv2 and grounds them to segment-specific captions encoded by SigLIP2 through segment-caption alignment on pseudo-labeled data from GSV-Cities and ScanNet. The grounded tokens are aggregated via an optimal transport-based module to produce compact retrieval descriptors. By integrating visual and textual cues at the segment level, SegCapVPR learns semantically enriched and spatially aware representations that improve robustness under challenging conditions. Experiments on indoor and outdoor benchmarks demonstrate substantial gains on Baidu Mall and AmsterTime, while remaining competitive on Pitts30K-Test, and MSLS-Val, establishing a unified paradigm for multimodal (image + text) and cross-modal (text -> image) VPR.

Abstract

At a time of technology-driven geopolitics, data diplomacy is quickly emerging as a means employed by nation states to achieve the goals of traditional diplomacy by attempting to territorialise data sovereignty through data governance laws and diplomatic negotiations. Data diplomacy is closely linked to the expression of digital sovereignty where data as a powerful resource enhances political sovereignty of the Westphalian nation state. Within the global context of growing trade frictions, influence of big tech and disputes over disruptive technologies, data diplomacy has evolved from an amalgamation of digital diplomacy and economic diplomacy and is being deployed in varied ways and approaches by dominant players like the US, the EU, China as well as middle powers like India to redefine the terms of engagement with other actors, both public and private in the form of digital economy agreements, digital trade chapters, etc. The paper explores the determining elements of data diplomacy as an emerging foreign policy practice that has complex interconnections with domestic factors such as internal regulatory capacity, level of technological and economic development, constitutional provisions and political narratives that influence foreign policy choices. With differences cropping up between like-minded allies like the US and the EU on data free flows and data privacy, data diplomacy is evolving into a partnership based transaction characterised by a complex layering of hard and soft contractual commitments on data localisation and data sharing. The paper goes beyond the existing free data flow vs data sovereignty narrative to differentiate how data diplomacy is now evolving into a kind of strategic and tactical engagement to forge issue based coalitions between nation-states based on power differentials over normative commitments. The paper also critically examines how data diplomacy means differently to the global north and global south and its impact on governing coming-of-age data intensive technologies like AI.