AgriScope is a unified pixel‑grounded multimodal framework designed for agricultural image understanding, supporting image‑level, region‑level, and pixel‑level tasks such as grounded caption generation, referring expression segmentation, and multi‑turn multimodal interaction. It incorporates biologically specialized semantic representations with dense spatial grounding through biological‑semantic encoding, dense spatial representations, and pixel decoding. The authors also introduce AgriGround, a large‑scale dataset of over 500K images and 11M instruction‑following samples, created via an automatic annotation pipeline that combines caption generation, phrase‑level grounding, segmentation mask generation, and task‑oriented instruction synthesis to provide densely grounded supervision for agricultural vision‑language learning.
The paper introduces SwitchSD, an adaptive framework that treats copying as a latent control signal in large language model decoding. By training lightweight probes on internal representations, SwitchSD accurately detects genuine copy intent (AUC > 0.99) and dynamically switches between neural drafting and context-based copying. Experiments on Llama and Qwen models show up to 15 % throughput gains over state‑of‑the‑art baselines such as EAGLE3.
MERIT‑Rank introduces a multi‑trajectory reasoning framework for text reranking, combining complementary reasoning paths to enhance robustness against errors. It defines a Multi‑Trajectory Reasoning Space (MTRS) and a joint reranker that merges these perspectives into a single ranking decision. The Progressive Rank Policy Optimization (PRPO) training scheme stabilizes reasoning trajectories and progressively improves ranking quality, yielding superior performance on both reasoning‑intensive and traditional retrieval benchmarks, with a 4B model outperforming larger rerankers on BRIGHT.
Recent memory-augmented repository-level program repair methods reuse historical repair experiences to improve LLM-based issue resolution. However, our analysis reveals three limitations in existing r...
The paper proposes a novel unsupervised safety detection method for large language models that relies on local sparsity in a linear representation space recovered via a sparse autoencoder. By masking SAE neurons based on shared active support among nearby points, the authors develop a locally masked anomaly detection framework with theoretical backing. Experiments across multiple architectures and datasets—including capability‑testing and safety‑specific sets—show that using only 1–2% of SAE neurons and a small amount of out‑of‑distribution data yields near‑optimal safety detection performance.
Cooley has developed GO Public, an IPO tool powered by ChatGPT Work, to infuse intelligence into the initial public offering process. The platform assists lawyers by identifying potential issues early, allowing them to concentrate their judgment on the most critical aspects of the IPO. By automating early-stage analysis, GO Public aims to streamline and improve the overall efficiency of IPO preparation.
SETTer is a transformer-based model designed for long‑term multivariate time‑series forecasting. It introduces decoupled self‑attention and hybrid masking to better capture short‑ and long‑term patterns across time and channel dimensions, while adding simple explainable structures to highlight discriminative patterns. Experiments on real‑world benchmarks show that a single‑layer SETTer outperforms state‑of‑the‑art models in 88% of scenarios.
I built four AI retrieval architectures on a laptop and benchmarked them against the same set of documents and questions. Here’s what the results taught me about the trade-offs between plain RAG, grap...
By Arijit Ghoshal
The paper introduces the Profiling, Investigation, and Judgment (PIJ) benchmark, which contains 2,500 real homicide cases from five countries to evaluate large language models (LLMs) on pre‑arrest criminal investigation tasks. It assesses LLMs across criminal profiling, crime process reconstruction, and sentence prediction, revealing that performance drops as tasks require more implicit reasoning about unknown suspect profiles. The study finds that LLMs lag behind human experts, especially in inferential categories like motivation and victim‑offender relationships, and exhibit biases in gender, age, and motive attribution.
MaSCoD is a multi‑agent framework that explicitly addresses the premature omission of causal relations by first organizing candidate third variables and local structural patterns before making direct‑edge judgments. Using GPT‑5.4 and GPT‑4o as backbones, the study evaluates MaSCoD on Auto‑MPG, DWD, and Sachs datasets, finding that the Full phase—where structural hypotheses are supplied early—generally improves recall and F1 scores compared to a No Phase 1 approach, though it can also raise false‑positive rates. Ablation experiments reveal that providing both structural and contextual information does not always outperform providing only one, and that the benefits of the Full phase vary across datasets and backbones.
whyItMatters":"The paper demonstrates that explicitly pre‑organizing structural context can improve causal graph generation performance, highlighting the importance of designing omission control mechanisms in causal discovery with large language models."
VākQA is a newly introduced benchmark for Telugu spoken factoid question answering, comprising 2,001 question‑answer pairs across six domains, 2.53 hours of speech audio, bilingual transcriptions, and human‑verified reference answers. The study validates automatic evaluation methods against human judgments, finding that Gemini‑as‑a‑judge best approximates human ratings but is inconsistently strict, while open‑weight judges tend to penalize correct Telugu answers that differ in surface form. Using this validated setup, the authors benchmark proprietary and open‑weight models, highlighting challenges such as cultural specificity loss in translation, phonetic confusions from speech input, and compounded errors from cascaded ASR‑MT pipelines.
The paper examines how large language model (LLM) based graphical user interface (GUI) agents respond to digital nudges. Using a randomized online shopping experiment with 3,600 agents across six frontier models, it finds that agents are vulnerable to both automatic and reflective nudges. The study shows that the agents’ reasoning configuration moderates these effects in opposite directions—reducing susceptibility to automatic nudges while increasing it to reflective social influence nudges—and that this redirection is systematically linked to model scale.
SnapPhysics is a training‑free framework that reconstructs 3D objects and estimates their physical properties—such as mass, friction, and center of gravity—from a single image. It combines instance‑level 3D reconstruction with a physics‑aware scene graph that encodes inter‑object relationships, providing structured context for vision‑language model reasoning. Experiments on 3D‑FRONT and real captured scenes show significant improvements over prior methods, reducing errors in mass estimation and enhancing scene‑level F‑Score.
The paper introduces a context‑aware neural grapheme‑to‑phoneme (G2P) system for unsegmented languages like Japanese, combining a discriminative conditional random field over a dictionary‑based word lattice with large language model‑generated training data. By generating over two million synthetic sentences, the method addresses data scarcity and achieves superior performance compared to traditional morphological analyzers and neural sequence models. On the Joyo‑Kanji‑Yomi benchmark, it attains 99.62% target word reading accuracy, 0.32% target word phoneme error rate, and 0.14% sentence phoneme error rate.
DeliveryGym is a 3D reinforcement learning environment that simulates continuous courier shifts, integrating multimodal tool interaction and persistent world dynamics to compute trajectory rewards based on simulator events. It provides feedback on resource consumption—time, energy, and money—across entire delivery trajectories, enabling agents to learn planning that balances immediate task success with long‑term constraints. The environment also adapts future training shifts to a policy’s weaknesses while keeping evaluation fixed, demonstrating that both learning from complete shifts and targeted training improve agent performance on complex delivery tasks.
The paper examines when multi‑agent collaboration is truly beneficial as large language models grow more capable. It finds that multi‑agent systems yield systematic advantages mainly for long‑horizon tasks with sparse dependencies, while single‑agent approaches excel in tightly coupled, sequential workflows. The authors introduce SAIGE, a lightweight, graph‑based collaboration framework that balances context efficiency and performance, demonstrating that adding more agents or deeper recursion does not always improve outcomes.
The paper "SoK: Trading Agents or Market Crashers? Dissecting Robustness and Security Failures in Academic Financial LLM Trading Schemes" introduces FARSIGHT, a framework that evaluates financial LLM agents on robustness to market turbulence and security against three attack types. Applying FARSIGHT to 15 academic schemes reveals that 80% fail at least one robustness metric and all exhibit security vulnerabilities, highlighting the risk that a single compromised agent can trigger market-wide crashes.
Autoregressive image generation has emerged as a paradigm for multimodal AI systems due to its compatibility with transformer-based LLM serving infrastructures. However, generating thousands of visual...
MiX: Micro-Inverted-Scaling for End-to-End Low-Bit Vision-Language Model Acceleration proposes a new quantization format that inverts the traditional microscaling approach by assigning private exponents to each element and a shared mantissa. The adaptive dual-format MiX-MX inference framework maps this format to a custom accelerator, replacing multipliers with shifters. Evaluations show that 4.5-bit MiX matches or surpasses NVFP4 accuracy on multimodal benchmarks while improving area efficiency by 25% and delivering 2.3–4.5× speedup with 1.4–2.9× energy reduction compared to the Focus accelerator.
The paper draws a parallel between AI model development and population genetics, treating successive model generations as analogous to sexual and asexual reproduction. It demonstrates that training models on peers’ outputs reproduces classic genetic processes such as the Wright–Fisher model, while combining parents’ weights can either cancel or preserve inherited advantages depending on the method. Experiments across recurrent, feedforward, and language models confirm these analogies and reveal architecture‑specific biases, including the Fisher‑Muller effect and reproductive isolation when lineages learn conflicting conventions.
By Giorgio F. Gilestro