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Grounding large language models (LLMs) in domain-specific tasks like post-hoc dash-cam driving video analysis is challenging due to their general-purpose training and lack of structured inductive biases. As vision is often the sole modality available for such analysis (i.e.

Assume |S|= m, for some m>0. Then, corresponding set of incorrectly-labelled examples is of size | S|= m(k 1). Let D S, be any distribution over m (k 1). We show that we can construct a distribution D S S over m k, for which the guarantee in the Lemma holds. First, observe that there must exist a m and b1,...,bm (k 1), such that for all i,j, we have D(i,j) = a(i)bi(j).

''Noisy'' datasets (regimes with low signal to noise ratios, small sample sizes, faulty data collection, etc) remain a key research frontier for classification methods with both theoretical and practical implications. We introduce FINDER, a rigorous framework for analyzing generic classification problems, with tailored algorithms for noisy datasets. FINDER incorporates fundamental stochastic analysis ideas into the feature learning and inference stages to optimally account for the randomness inherent to all empirical datasets. We construct ''stochastic features'' by first viewing empirical datasets as realizations from an underlying random field (without assumptions on its exact distribution) and then mapping them to appropriate Hilbert spaces. The Kosambi-Karhunen-Loéve expansion (KLE) breaks these stochastic features into computable irreducible components, which allow classification over noisy datasets via an eigen-decomposition: data from different classes resides in distinct regions, identified by analyzing the spectrum of the associated operators. We validate FINDER on several challenging, data-deficient scientific domains, producing state of the art breakthroughs in: (i) Alzheimer's Disease stage classification, (ii) Remote sensing detection of deforestation. We end with a discussion on when FINDER is expected to outperform existing methods, its failure modes, and other limitations.

Despite continuous advancements in the capabilities of large language models (LLMs), numerical reasoning remains a challenging area. Techniques like chain-of-thought prompting, tree-of-thought prompting, and program-of-thought prompting guide LLMs through intermediate reasoning steps. Although in-context learning with few-shot prompting has improved performance, LLMs still lag behind state-of-the-art models on financial numerical reasoning datasets such as FinQA and ConvFinQA. In this work, we introduce FINDER, a novel two-step framework, to enhance LLMs' capabilities in financial numerical reasoning. The first step utilizes a generative retriever to extract relevant facts from unstructured data, including both text and tables. This is followed by context-aware Program of Thought prompting with dynamic selection of in-context examples. Our model FINDER achieves a new state-of-the-art performance on both the FinQA and ConvFinQA datasets, surpassing previous benchmarks with execution accuracy improvements of 5.98% and 4.05%, respectively.

The growing volume of scientific literature makes it challenging for scientists to move from a list of papers to a synthesized understanding of a topic. Because of the constant influx of new papers on a daily basis, even if a scientist identifies a promising set of papers, they still face the tedious task of individually reading through dozens of titles and abstracts to make sense of occasionally conflicting findings. To address this critical bottleneck in the research workflow, we introduce a summarization feature to BIP! Finder, a scholarly search engine that ranks literature based on distinct impact aspects like popularity and influence. Our approach enables users to generate two types of summaries from top-ranked search results: a concise summary for an instantaneous at-a-glance comprehension and a more comprehensive literature review-style summary for greater, better-organized comprehension. This ability dynamically leverages BIP! Finder's already existing impact-based ranking and filtering features to generate context-sensitive, synthesized narratives that can significantly accelerate literature discovery and comprehension.

Test-time scaling has emerged as an effective approach for improving language model performance by utilizing additional compute at inference time. Recent studies have shown that overriding end-of-thinking tokens (e.g., replacing "" with "Wait") can extend reasoning steps and improve accuracy. In this work, we explore whether a dedicated continue-thinking token can be learned to trigger extended reasoning. We augment a distilled version of DeepSeek-R1 with a single learned "<|continue-thinking|>" token, training only its embedding via reinforcement learning while keeping the model weights frozen. Our experiments show that this learned token achieves improved accuracy on standard math benchmarks compared to both the baseline model and a test-time scaling approach that uses a fixed token (e.g., "Wait") for budget forcing. In particular, we observe that in cases where the fixed-token approach enhances the base model's accuracy, our method achieves a markedly greater improvement. For example, on the GSM8K benchmark, the fixed-token approach yields a 1.3% absolute improvement in accuracy, whereas our learned-token method achieves a 4.2% improvement over the base model that does not use budget forcing.

Our proposal is on a new stochastic optimizer for non-convex and possibly non-smooth objective functions typically defined over large dimensional design spaces. Towards this, we have tried to bridge noise-assisted global search and faster local convergence, the latter being the characteristic feature of a Newton-like search. Our specific scheme -- acronymed FINDER (Filtering Informed Newton-like and Derivative-free Evolutionary Recursion), exploits the nonlinear stochastic filtering equations to arrive at a derivative-free update that has resemblance with the Newton search employing the inverse Hessian of the objective function. Following certain simplifications of the update to enable a linear scaling with dimension and a few other enhancements, we apply FINDER to a range of problems, starting with some IEEE benchmark objective functions to a couple of archetypal data-driven problems in deep networks to certain cases of physics-informed deep networks. The performance of the new method vis-\'a-vis the well-known Adam and a few others bears evidence to its promise and potentialities for large dimensional optimization problems of practical interest.

In various real-world robot applications, MOS describes the problem of locating multiple objects efficiently [1], in domains such as warehouse management [2, 3], construction inspection [4], or hospitality [5, 6, 7], and retail assistance [8, 9]. Existing MOS methods can be categorized into: 1) probabilistic planning (PP) [1, 10, 11, 12], and 2) deep reinforcement learning (DRL) methods [13, 14, 15, 16, 17, 18, 19, 20]. PP methods utilize Partially Observable Markov Decision Processes (POMDPs) to estimate belief states and plan actions under uncertainty in object locations, while DRL methods optimizes action selection using a reward function [21]. However, both approaches face challenges such as inefficient exploration due to limited semantic modeling between objects and scenes [18], and poor generlization caused by the sim-to-real gap [19]. Recently, Large Foundation Models (LFMs) such as vision-language models (VLMs) and large language models (LLMs) have been applied to single object search (SOS) tasks by using either: 1) VLMs (e.g., CLIP, BLIP, etc.) to generate scene-level embeddings that capture the semantic correlations between the robot's environment and the target object to guide the robot towards regions with high target object likelihood [19, 22, 23, 24, 25]; or, 2) VLMs/LLMs to generate scene captions that describe both the spatial layout and semantic details of the robot's environment which are then used to plan the robot's actions [26, 27, 28, 29, 30, 31, 32]. However, these SOS methods have limitations: 1) they cannot be directly applied to MOS, as they lack explicit mechanisms to track and reason about multiple objects simultaneously, and 2) scene-level embeddings are often noisy and coarse [33], which cannot be effectively applied in object-dense environments. In such cases, fine-grained, object-level embeddings are needed. In this paper, we introduce Finder, the first MOS approach that leverages VLMs to locate multiple target objects in various unknown environments.