This paper addresses the fundamental problem that correlational sentiment analysis cannot distinguish genuine economic associations from spurious statistical artifacts in financial markets. The core contribution is a refutation-validated framework for aspect-based sentiment analysis that combines net-ratio sentiment scoring with four robustness tests—placebo, random common cause, subset stability, and bootstrap validation—to filter false discoveries in high-dimensional sentiment-return analysis. This matters because investment strategies built on spurious correlations can lead to systematic losses, and regulators increasingly demand explainable AI systems with auditable validation.

Preference alignment typically requires expensive weight-updating training like RLHF or DPO, which lacks mechanistic interpretability. This paper proposes DSPA, an inference-time method that dynamically steers sparse autoencoder (SAE) features based on prompt content without modifying base-model weights. By computing a sparse conditional-difference map $\mathbf{A}$ from preference triples that links prompt features to generation-control features, DSPA edits only token-active latents during decoding. The method achieves competitive open-ended generation quality with up to $4.47\times$ fewer alignment-stage FLOPs than training-based alternatives, while offering direct auditability of which features are modified and revealing that preference directions are dominated by discourse and stylistic signals.

Discrete diffusion models have been limited to simplistic noising schemes like uniform corruption or masking, restricting their ability to leverage semantic structure in large vocabularies. This paper introduces GDDS (Generalized Discrete Diffusion from Snapshots), a framework supporting arbitrary continuous-time Markov chain noising processes via exact uniformization-based sampling and a tractable snapshot-level ELBO. The work achieves state-of-the-art results on large-scale language modeling tasks, claiming to surpass autoregressive baselines for the first time at this scale.

KG-Hopper addresses Knowledge Base Question Answering (KBQA) by training compact 7B LLMs to perform multi-hop reasoning over Knowledge Graphs in a single inference round. Unlike sequential multi-step approaches that suffer from error cascades, it embeds the entire KG traversal process into a unified "thinking" stage using reinforcement learning. The core innovation is using GRPO (Group Relative Policy Optimization) with composite rewards to teach models to autonomously invoke retrieval tools via special tokens and reason across multiple hops without predefined pipelines.

This paper tackles the lack of explicit memory mechanisms in transformers by introducing Mixture of Chapters (MoC)—a learned bank of 262K latent memory tokens accessed via cross-attention. To scale memory without prohibitive costs, the authors partition the bank into chapters and route each input sequence to a sparse subset (top-64), reducing complexity from $O(L \cdot N_m)$ to $O(L \cdot k \cdot T)$. The work demonstrates that explicit associative memory can serve as a new axis of scaling, showing improved knowledge retention when transitioning from pretraining to instruction fine-tuning.

Designing high-performance system heuristics traditionally requires human experts to navigate multi-step conceptual shifts. This paper introduces Engram, an agentic architecture that sidesteps the 'coherence ceiling' of single-context LLM agents and the 'evolutionary neighborhood bias' of code-mutation systems by decoupling long-horizon exploration into sequential agent handoffs. Each agent distills findings into a persistent Research Digest, enabling cumulative progress without context degradation.

ViCLSR adapts supervised contrastive learning (SimCSE-style) to Vietnamese NLU by converting NLI entailment and contradiction pairs into positive and negative training signals. Built on XLM-R Large (550M), the framework improves sentence embeddings for low-resource Vietnamese, reporting gains of +6.97% F1 over PhoBERT on ViNLI and state-of-the-art results across five downstream tasks including fact-checking and machine reading comprehension.

This paper investigates whether neural text-to-speech systems capture consonant-induced F0 perturbation—fine-grained phonetic effects where voiceless obstruents raise and voiced obstruents lower fundamental frequency relative to sonorants. The authors propose a segmental-level prosodic probing framework comparing Tacotron 2 and FastSpeech 2 against natural speech, stratifying by lexical frequency to test memorization versus abstraction. This matters because TTS evaluation often misses sub-phonemic articulatory detail that distinguishes human-like phonetic competence from surface pattern matching.

This paper evaluates three inference-time strategies—self-consistency with temperature/top-p sampling, dual-model cross-verification, and iterative self-reflection—to improve multi-step reasoning in LLMs without parameter updates. The core premise is that aggregating diverse reasoning traces or validating across models yields more reliable outputs than single-pass decoding. The work addresses a practical need for deployment scenarios where retraining is infeasible, though the experimental scope is limited by unclear model specifications and dataset choices.

This paper tackles multimodal hate speech detection where hateful intent emerges from complex interactions between text and images—what the authors call "more than the sum of its parts." The core innovation is the Stratified Multimodal Interaction (SMI) paradigm, which categorizes eight distinct cross-modal interaction patterns into three difficulty levels (Easy, Normal, Hard), coupled with the ARCADE framework that simulates an asymmetric courtroom debate between Prosecutor, Defender, and Judge agents to decipher subtle intent shifts. This matters because current detection systems fail when hateful content is constructed implicitly through benign-seeming modalities that only become toxic in combination.

This paper tackles logical context poisoning—the degradation of LLM responses when flat, linear conversation structures force topically distinct threads to accumulate in a single unbounded context window. The core idea is the Conversation Tree Architecture (CTA), which models conversations as a directed rooted tree $\mathcal{T}=(V,E,r,W)$ where each node $v \in V$ maintains an isolated local context window $w_v$. Structured flow operations—downstream passing $\phi_{\downarrow}$, upstream merging $\psi_{\uparrow}$, and volatile nodes—govern how context moves between branches. This matters because current interfaces offer no middle ground between discarding context (new chat) and accumulating noise (linear threads).

Extracting hypotheses and their supporting statistical evidence from full-text scientific articles is challenging due to document length and the distribution of scientific arguments across sections. This paper proposes a two-stage retrieve-and-extract pipeline that first links an abstract finding to its corresponding hypothesis, then extracts the statistical evidence supporting that hypothesis. Through controlled ablations varying context quantity ($k \in \{5, 10, 20\}$), retrieval quality (standard RAG, reranking, fine-tuned retriever), and oracle paragraph settings, the authors demonstrate that hypothesis extraction is primarily bounded by retrieval quality, while evidence extraction faces persistent extractor limitations even with perfect paragraph selection.

This paper formalizes the transformer context window as an I/O page and proves that tool-augmented agents with indexed external memory achieve exponential retrieval cost savings over sequential scanning: $\mathcal{O}(\log_b N)$ versus $\Omega(N)$ page reads. The authors validate these predictions experimentally across three content types and identify "parametric memory competition" as a failure mode where models bypass retrieval protocols for familiar content.

LongCat-Flash-Prover is a 560B-parameter MoE open-source model targeting native formal reasoning in Lean4. The core innovation is decomposing formal theorem proving into three agentic capabilities—auto-formalization, sketching, and proving—trained via a Hybrid-Experts Iteration Framework and a novel RL algorithm called HisPO. The work claims state-of-the-art results on MiniF2F-Test (97.1%), ProverBench (70.8%), and PutnamBench (41.5%) with remarkably low inference budgets compared to prior open-source provers.

This paper addresses selection bias (position and label bias) in large language models during discrete-choice tasks like multiple-choice questions and pairwise evaluation. The authors propose Permutation-Aware GRPO (PA-GRPO), which extends Group Relative Policy Optimization by treating different permutations of the same question as a single training group rather than independent instances. The method enforces semantic consistency across permutations through two mechanisms: a cross-permutation advantage that computes rewards relative to the group mean, and a consistency-aware reward that penalizes disagreement across permutations. Experiments across seven benchmarks and three models (Llama-3.1-8B, Qwen3-8B, Qwen3-32B) demonstrate that PA-GRPO reduces selection bias while maintaining accuracy.

This paper investigates why compressing different weight matrices in transformers leads to wildly different outcomes—from negligible impact to 20,000× perplexity increases. The authors map this structural sensitivity across five architectures, revealing that early-layer MLP up-projections are catastrophically fragile while value projections are nearly free to compress. Using Lyapunov stability theory, they explain how residual connections contract errors, and they provide machine-checked formal bounds in Lean 4 to guarantee per-matrix approximation quality.

NVIDIA introduces Nemotron 3, a family of open language models (Nano, Super, Ultra) built on a hybrid Mamba-Transformer MoE architecture. The core innovation is using selective attention layers combined with Mamba-2 state space layers to achieve high throughput while maintaining accuracy. Key technical contributions include LatentMoE (dimensionality-reduced expert routing), NVFP4 training for efficiency, and multi-environment RL post-training. The paper positions these models as optimized for agentic AI with up to 1M token contexts and granular inference-time reasoning budget control.