未步英特尔后尘！AMD Q3营收超预期增45%，数据中心收入近翻倍，Q4指引提速又新高｜财报见闻
华尔街见闻Q3AMD营收较Q2所创纪录高15.5%，EPS同比增74%；客户端收入同比增7%、低于预期，游戏收入高于预期、但同比降40%，嵌入式收入超预期增29%；自由现金流降64%至5.5亿美元、远逊预期，压力主要来自库存增近四倍至30亿。Q4营收指引中值同比增66%至170亿美元，较分析师预期高7亿、较英特尔同期指引高近10%。CEO称，随着Helios系统和Venice CPU扩产，四季度增长料进一步
LENS-GRF: Permutation-Invariant Lesion Evidence Network with Gated Residual Fusion for Acne Severity Grading and Multi-Rater Clinical Oracle Analysis
arXiv cs.AIarXiv:2610.00294v2 Announce Type: replace-cross
Abstract: Localized lesion evidence may improve automated acne severity grading, but detector errors and differences in clinical grading criteria can limit generalization. We evaluated LENS-GRF, a lesion evidence network with permutation-invariant aggregation and gated residual fusion that combines a global Vision Transformer with lesion-region embeddings derived from YOLO11s proposals. Fully automated four-class grading was evaluated on 292 held-out ACNE04 images and, without external adaptation, on 200 PLSBRACNE01 participants represented by 600 facial views. Annotation-assisted inference used registered lesion boxes from three dermatologists in a matched subset of 148 participants. Performance measures included accuracy, macro-averaged precision, sensitivity, and one-versus-rest specificity. Paired comparisons used exact McNemar tests with Holm adjustment. On ACNE04, automated LENS-GRF achieved 80.82% accuracy, 78.80% macro precision, 78.92% sensitivity, and 92.78% specificity. With supplied lesion boxes, internal accuracy averaged 95.89% across three seeds. On the full external cohort, automated LENS-GRF achieved 35.00% accuracy, compared with 42.50% for an independently trained global baseline. External macro precision, sensitivity, and specificity for LENS-GRF were 50.52%, 34.31%, and 78.19%, respectively. In the matched subset, accuracy using Doctor A's lesion annotations was 47.97%, compared with 35.14% for automated inference (unadjusted $p = 0.0094$; Holm-adjusted $p = 0.0846$). Strong internal performance with supplied lesion annotations did not translate into reliable automated grading on the external cohort. More reliable lesion detection, modelling that accounts for differences in grading criteria, and independent validation are needed before clinical use.
Proofs Without Nominals: G\"odel's Ontological Argument, its Shallow Embedding, and the Open Questions of the Monatshefte Notes
arXiv cs.AIarXiv:2609.36279v5 Announce Type: replace-cross
Abstract: The shallow embedding of higher-order modal logic in classical higher-order logic, used in Benzm\"uller and Scott's Notes on G\"odel's and Scott's variants of the ontological argument, reaches beyond the modal object language: its quantifiers range over domains containing the nominals and satisfaction operators of hybrid logic. A proof using one proves a theorem of the embedding, not necessarily of the modal logic. Whether a result is one of the modal logic can be settled by replaying it in an explicit calculus, or, more cheaply and for all results at once, by analysing what the embedding's proofs substitute. Every statement the Notes prove has a proof in the embedding substituting nothing outside the object language, 294 of them explicit. The Notes' own proofs substitute none either; what it flags are terms a prover introduced. A soundness theorem over general models closed under the object language, and a sentence of that language the embedding proves but which fails in such a model, fix what the certificate says.
The Notes' three open questions are settled too, without nominals, but by the empty instance of the conjunction axiom, too cheaply to convince. Generalised to G\"odel's "any number of summands", the axiom covers the empty conjunction and the singleton. This article restricts it to at least two different conjuncts, a non-degenerate reading of G\"odel's footnote, and settles them again without nominals. In effect only the empty conjunction is excluded; the singleton comes back with the other axioms. In one variant the restriction refutes the possible existence of a God-like being; the answers return, with no side condition, once the Notes' non-emptiness clause is confined to G\"odel's essence. Every theorem here is verified in Isabelle/HOL and independently in Lean 4, the separating model in its propositional part; the countermodels are Nitpick's, checked by the build.
Matryoshka Hash Representations for Model-Aware Compact Semantic Retrieval
arXiv cs.AIarXiv:2609.07276v2 Announce Type: replace-cross
Abstract: Retrieval-augmented generation (RAG) depends on dense retrieval: each document is stored as a learned vector, and queries are answered by nearest-neighbor search. Keeping one full-precision vector per document dominates index cost at corpus scale, so systems replace each vector with a short code of a few bytes (quantization). Standard quantizers such as product quantization (PQ) pick the code that best reconstructs the original vector, but reconstruction is only a proxy for retrieval: at a few bytes per document, a model-aware code trained on the retriever's ranking signal uses the budget better. A single code is even more useful if its short prefixes are each directly searchable, letting a deployment set its efficiency-quality operating point without re-encoding the corpus. But training all prefixes under one objective makes the early bits a compromise across budgets: short codes improve while the full-width code degrades. Binary codes sharpen the conflict, since a gradient step either flips a stored bit or leaves it unchanged. We introduce Matryoshka Hash Representations (MHR), a two-stage procedure that separates full-width training from prefix organization. MHR first learns a longer binary code, then freezes the model and trains zero-initialized residual code adaptors for directly searchable prefixes. Documents are stored at one bit per coordinate, queries keep continuous logits as in PQ, and search runs on FAISS FastScan. Trained on MS MARCO and zero-shot transferred to seven BEIR datasets, one MHR code reaches 0.502 NDCG@10 at 16 bytes and 0.556 at 32 bytes, against 0.404 and 0.524 for the best compact baselines; the gains hold across training seeds, on all 13 public BEIR datasets, and against off-the-shelf binary Matryoshka embeddings with stronger backbones. The same code also helps shortlist candidates for reranking and prune low-storage graph indexes such as LEANN.
A Study of ASR Adaptation and Representation Dimensionality Reduction in Persian Speech Emotion Recognition Using Whisper
arXiv cs.AIarXiv:2608.05165v3 Announce Type: replace-cross
Abstract: Speech Emotion Recognition (SER) in low-resource languages remains a challenging problem due to limited labeled data. In this work, we study the use of Whisper for Persian SER with a particular focus on representation dimensionality reduction and language-specific model adaptation. We propose a SER framework in which frame-level embeddings extracted from the Whisper encoder are reduced in dimensionality using PCA, eliminating the need for learned projection layers and substantially reducing the number of trainable parameters. The reduced representations are aggregated using an attention-based pooling mechanism and classified with a lightweight prediction head. In addition, we investigate whether fine-tuning Whisper on a Persian automatic speech recognition (ASR) task improves downstream SER performance. Experiments conducted on the ShEMO dataset under a speaker-independent evaluation protocol show that PCA-based dimensionality reduction consistently improves emotion recognition performance while reducing training latency and memory usage. ASR fine-tuning yields only modest gains for SER, suggesting limited transfer from language adaptation to emotion-related representations under the evaluated conditions. These findings provide practical insights into the efficient use of large pretrained speech models for emotion recognition in low-resource languages.
SemFlowRAG: Directed Semantic Flow from Abstraction to Evidence for Complex Reasoning
arXiv cs.AIarXiv:2606.28447v2 Announce Type: replace-cross
Abstract: Retrieval-Augmented Generation (RAG) enhanced by Knowledge Graphs has shown promise in complex multi-hop reasoning tasks. However, existing graph-based retrieval methods typically rely on flat, undirected topologies. During the retrieval process, the probability flow often gets trapped in high-degree abstract concept nodes which we define as ``probability black holes'', leading to semantic drift and noise accumulation. To address this, we propose SemFlowRAG, a framework that reconstructs the flat retrieval space into a corpus-adaptive semantic gradient graph. This data-driven self-organization enables a hierarchical structure to emerge naturally from the data distribution, capturing the intrinsic semantic granularity of the corpus to suppress structural noise. By quantifying the semantic abstractness of entities through the embedding variance of their associated passages, we transform static undirected edges into directed semantic constraints. Furthermore, we design an abstractness-guided directed PageRank algorithm that forces the retrieval trajectory to follow a ``high-to-low semantic abstractness'' gradient. This mechanism ensures layer-by-layer evidence convergence, smoothly guiding the retrieval process from abstract concepts to specific document evidence. Extensive experiments on complex QA datasets demonstrate that SemFlowRAG effectively mitigates the ``probability black holes'' issue, outperforming existing baselines in both retrieval and downstream reasoning performance.
NIV: Neural Axis Variations for Variable Font Generation
arXiv cs.AIarXiv:2606.05261v2 Announce Type: replace-cross
Abstract: Variable fonts enable continuous variation of glyph geometry along semantic design axes such as weight, width, slant, and optical size. However, constructing a variable font from a static font remains a labor-intensive process requiring expert typographic design and manual specification of glyph variation data. We introduce NIV (Neural Axis Variations), a method that automatically converts a static font into a fully functional variable font. Given glyph outlines and a set of desired design axes, NIV predicts per-point displacements. The model operates directly on vector glyph geometry and employs a novel Property Embedding mechanism that captures interactions between multiple axes, enabling consistent multi-axis variation within a unified framework. We train NIV on a newly constructed dataset derived from variable Google Fonts, comprising over one million variation tuples. The resulting model generalizes across unseen code points, unseen font styles, high-complexity CJK glyphs, and even out-of-distribution handwriting inputs. The generated outputs are standard variable font files supporting continuous interpolation via existing rendering engines. To facilitate research, we release the dataset, the complete training and inference implementation, and trained models at https://github.com/ndvbd/NIV. Beyond typography, our approach demonstrates how structured geometric objects with continuous parametric variation can be synthesized using neural deformations.
Do Neural Retrievers Prefer Certain Documents? Evidence of Learned Relevance Priors
arXiv cs.AIarXiv:2606.02814v2 Announce Type: replace-cross
Abstract: Neural retrievers are trained to estimate query-document relevance from annotated query-document pairs. Yet annotation protocols may not purely reflect relevance: they select only a subset of documents for labeling, and this selection can favor certain document types over others. We investigate whether supervised bi-encoder retrievers implicitly learn a document-level relevance prior: a query-independent signal encoded in their representation space as a side effect of training on annotated data. We estimate this prior by training simple classifiers on frozen document embeddings and evaluate three state-of-the-art retrievers across multiple IR benchmarks. We find that supervised neural retrievers encode relevance priors that generalize to unseen documents and are consistent across models. These priors are associated with a findability gap: documents with lower prior are systematically harder to retrieve, even when genuinely relevant. This effect appears in supervised dense retrievers but is weaker and less consistent in BM25, and it persists under controlled matched-document comparisons. Using LLM-based explanations, we find that judged-relevant documents tend to be comprehensive, self-contained summaries of mainstream topics, while niche, fragmentary, or highly technical content is often left unjudged. Retrievers appear to internalize this bias, ranking documents with these favored features higher than documents that lack them, independently of their actual relevance. Our findings expose a structural limitation of supervised retrieval: models trained on annotated data do not just learn relevance, but also the implicit document preferences in their training data.
Beyond Isotropy in JEPAs: Hamiltonian Geometry and Symplectic Prediction
arXiv cs.AIarXiv:2605.20107v2 Announce Type: replace-cross
Abstract: JEPAs often regularize one-view embeddings toward an isotropic Gaussian, implicitly baking Euclidean symmetry into the representation. We show that this is not merely a benign default. For a known structured downstream geometry $H\succ0$, the minimax and maximum-entropy covariance under a Hamiltonian energy budget is $(c/d)H^{-1}$, and Euclidean isotropy incurs a closed-form price of isotropy. More importantly, when the downstream geometry is unknown, no geometry-independent fixed marginal target is canonical: every fixed covariance shape can be maximally misaligned for some structured geometry. We further show that even oracle one-view marginals do not identify the JEPA view-to-view predictive coupling. Within continuous linear phase-space flows, stationarity of the lifted oracle law, momentum reversal, and fixed-unit kinematics uniquely select Hamiltonian dynamics. These results suggest that the structural bias in JEPAs should enter the cross-view coupling rather than a fixed encoder marginal. We instantiate this principle with \textbf{HamJEPA}, which encodes each view as a phase-space state $(q,p)$ and predicts view-to-view transitions with a learned Hamiltonian leapfrog map, while non-isotropic scale and spectral floors discourage collapse. In a deliberately headless token protocol, HamJEPA improves over SIGReg on CIFAR-100 by $+4.89$ kNN@20 and $+3.52$ linear-probe points at 30 epochs, and, over two training seeds at 80 epochs, by $+7.24$ kNN@20 and $+9.67$ linear-probe points relative to the full 2048-dimensional SIGReg representation. Matched non-symplectic and non-reversible controls retain the gain, so the experiments do not identify either property as individually necessary. On ImageNet-100, HamJEPA-$q$ improves by $+4.82$ kNN@20 and $+7.52$ linear-probe points at 45 epochs.
Text Corpora as Concept Fields: Providing Explainable Groundedness
arXiv cs.AIarXiv:2605.05103v4 Announce Type: replace-cross
Abstract: We introduce the \textbf{Concept Field} of a text corpus: a local drift field with pointwise uncertainty, estimated in sentence-embedding space from the deltas between consecutive sentences. Given a candidate sentence transition, we score its agreement with the field by $\zeta$, the mean absolute z-distance between the observed delta and the field's local statistical estimate, giving a quantitative measure of groundedness. The score is model agnostic and corpus-attributable, providing traces to corpus semantics either supporting or refuting groundedness. We drive the computation with the introduction of a \textbf{Vector Sequence Database (VSDB)} that stores embeddings together with sequence-position and next-delta metadata. We demonstrate this approach on two large-scale settings: groundedness detection over the U.S. Code of Federal Regulations, and ungroundedness (novelty) detection over Project Gutenberg, where we show Concept Fields achieve strong selective classification performance under a grounded / ungrounded / unsure triage policy. Unlike retrieval-centric baselines, the resulting coverage-risk behavior shows similar empirical coverage-performance trends in the two evaluated domains. Concept Fields provide a fast, lightweight, and interpretable signal for groundedness, complementary to LLM-as-judge and white-box detectors.
Bolek: A Multimodal Language Model for Molecular Reasoning
arXiv cs.AIarXiv:2605.02745v2 Announce Type: replace-cross
Abstract: Molecular property models increasingly support high-stakes drug-discovery decisions, but their outputs are often difficult to audit: classical predictors return scores without rationale, while language models can produce fluent explanations weakly grounded in the input molecule.
We introduce Bolek, a compact multimodal language model that grounds natural-language reasoning in molecular structure by injecting a Morgan fingerprint embedding into an instruction-tuned text decoder. Bolek is fine-tuned on molecular alignment tasks, including molecule description, RDKit descriptor prediction, and substructure detection, and on downstream reasoning over 15 TDC binary classification tasks using synthetic chains-of-thought anchored in concrete molecular features.
Across these tasks, Bolek outperforms its Qwen3-4B-Instruct base on all endpoints in yes/no mode and on 13 of 15 in chain-of-thought mode, raising mean ROC/PR AUC from 0.55 to 0.76. It also outperforms TxGemma-9B-Chat on 13 of 15 binary classification tasks despite being less than half its size. Bolek's explanations are more grounded than those of the baseline LLMs: it cites numerical descriptors 10-100x more often per chain-of-thought, and the cited values agree strongly with RDKit for key descriptors such as TPSA, MolLogP, and MolWt (Spearman rho = 0.87-0.91). Generalisation extends beyond the training panel: on 15 unseen TDC classification endpoints, Bolek matches TxGemma on five, and it produces non-trivial rank correlations on three held-out regression endpoints despite never seeing downstream regression during training.
These results suggest that targeted modality injection and reasoning supervision tied to verifiable molecular features can yield compact, auditable molecular reasoning models.
VLM2Rec: Resolving Modality Collapse in Vision-Language Models for Sequential Recommendation
arXiv cs.AIarXiv:2603.17450v3 Announce Type: replace-cross
Abstract: Sequential Recommendation (SR) learns sequence-level Collaborative Filtering (CF) from interactions, but ID-based models struggle with sparse data and cold-start items. In contrast, item titles and images can reveal preference continuity, such as a recurring color or style. Yet existing approaches miss visual continuity: small encoders take a single item as input, and Large Language Models (LLMs) read only titles. We therefore shift our focus to Large Vision-Language Models (VLMs), which can read a user's entire image sequence, not just its titles. However, VLMs inherently suffer from modality collapse: they over-rely on text and, as a result, overlook visual continuity for recommendation. We find that standard contrastive Supervised Fine-Tuning (SFT), essential for adapting VLMs to SR, further amplifies this collapse: updates follow the text gradient, visual discrimination weakens, and adding image embeddings to the text embeddings consistently lowers accuracy. We propose VLM2Rec with two objectives: Weak-modality Penalized Contrastive Learning strengthens each user's weaker modality, and Cross-modal Relational Topology Regularization aligns each user's relative similarity structure over the same items across the two modality spaces. Its variant, $\text{VLM2Rec}_{\text{plug}}$, reuses the learned item embeddings in SR backbones and thus serves at the latency of conventional SR models. Experiments show that VLM2Rec mitigates the collapse and consistently improves accuracy and robustness.
DARE: Aligning LLM Agents with the R Statistical Ecosystem via Distribution-Aware Retrieval
arXiv cs.AIarXiv:2603.04743v2 Announce Type: replace-cross
Abstract: Large Language Model (LLM) agents excel at automating data science workflows, yet often underutilize rigorous statistical methods in R. Existing Retrieval-Augmented Generation (RAG) approaches rely on semantic similarity, ignoring the data-distribution and structural characteristics crucial for determining a statistical method's applicability. To address this, we propose DARE (Distribution-Aware Retrieval Embedding), a representation learning framework incorporating these data characteristics into R function retrieval. Our contributions are fourfold: (i) RPKB, a curated knowledge base of 8,191 high-quality R functions; (ii) DARE, the distribution-aware retrieval framework; (iii) RCodingAgent, an R-oriented agent integrating DARE for end-to-end analysis; and (iv) RStatBench, a comprehensive benchmark of 100 statistical tasks. Experiments demonstrate that DARE improves NDCG@10 by 13.43 to 32.65 percentage points across evaluated embedding models, with a MiniLM-based DARE achieving 93.92% NDCG@10 alongside high efficiency. Furthermore, integrating DARE into RCodingAgent nearly doubles mean downstream accuracy on RStatBench, increasing from 31.8% to 62.2% across ten LLMs. This work helps bridge modern LLM automation with R's established statistical computing ecosystem.
Revisiting RAG Retrievers: An Information Theoretic Benchmark
arXiv cs.AIarXiv:2602.21553v2 Announce Type: replace-cross
Abstract: Retrieval-Augmented Generation (RAG) systems rely critically on the retriever module to surface relevant context for large language models. Although numerous retrievers have recently been proposed, each built on different ranking principles such as lexical matching, dense embeddings, or graph citations, there remains a lack of systematic understanding of how these mechanisms differ and overlap. Existing benchmarks primarily compare entire RAG pipelines or rely on aggregate ranking metrics (Recall, MRR, nDCG) that assume item-level independence, providing little guidance on redundancy or complementarity between retrievers. This work presents MIGRASCOPE, a Mutual Information based RAG Retriever Analysis Scope. We introduce principled metrics grounded in information theory to quantify retrieval quality, redundancy/synergy, and marginal contribution. Although the framework is designed to be applicable to any setting where a ground-truth answer can construct a pseudo chunk distribution, the present empirical study focuses on multi-hop question answering - a demanding setting where retriever complementarity is especially pronounced. On four corpora and thirteen SOTA retrievers spanning lexical, dense, and graph-based families, we show that (i) the MI-based divergence metric captures retriever quality dimensions not reflected by Recall or MRR, (ii) graph-based retrievers are redundant with each other while BM25 remains informationally complementary, and (iii) in the multi-hop QA setting, a carefully chosen ensemble guided by MI-based attribution can outperform any single retriever. We position these findings as task-specific insights and provide a reusable framework for retriever analysis that practitioners can re-estimate on their own target domains.
StretchTime: Adaptive Time Series Forecasting via Symplectic Attention
arXiv cs.AIarXiv:2602.08983v2 Announce Type: replace-cross
Abstract: Transformer architectures have established strong baselines in time series forecasting, yet they typically rely on positional encodings that assume uniform, index-based temporal progression. However, real-world systems, from shifting financial cycles to elastic biological rhythms, frequently exhibit "time-warped" dynamics where the effective flow of time decouples from the sampling index. In this work, we first formalize this misalignment and prove that rotary position embedding (RoPE) is mathematically incapable of representing non-affine temporal warping. To address this, we propose Symplectic Positional Embeddings (SyPE), a learnable encoding framework derived from Hamiltonian mechanics. SyPE strictly generalizes RoPE by extending the rotation group $\mathrm{SO}(2)$ to the symplectic group $\mathrm{Sp}(2,\mathbb{R})$, modulated by a novel input-dependent adaptive warp module. By allowing the attention mechanism to adaptively dilate or contract temporal coordinates end-to-end, our approach captures locally varying periodicities without requiring pre-defined warping functions. We implement this mechanism in StretchTime, a multivariate forecasting architecture that achieves state-of-the-art performance on standard benchmarks, demonstrating superior robustness on datasets exhibiting non-stationary temporal dynamics.
Embedding Consistency Regulation for Persistent Semantic Routing in Compact Language Models
arXiv cs.AIarXiv:2601.00543v2 Announce Type: replace-cross
Abstract: Language models are increasingly deployed under tight memory and compute constraints, where semantic routes (e.g., language, tone, or intent) may be assigned before decoding. However, these routes may not remain reflected in generated responses. We study this phenomenon as \emph{persistent semantic routing}. We investigate this property using Embedding Consistency Regulation (ECR) as a minimal probe. ECR derives a short discrete prefix for each input using a frozen embedding model and applies the same prefix construction during both training and inference. In controlled prefix-assignment experiments, ECR achieves 99.93\% $\pm$ 0.12\% language routing accuracy, while random, shuffled, and dummy controls remain near chance (34--36\%). This gap is not reliably captured by likelihood: some controls achieve competitive NLL despite losing routing consistency. We introduce Response Routing Recoverability (RRR), an encoder-independent diagnostic that tests whether assigned routes remain observable after generation. Routing recoverability persists across multilingual settings, unsupervised partitions, reduced precision, and transfer tasks. These findings establish routing persistence as a distinct and measurable property of language models, complementary to task performance and likelihood.
AgentTypo: Adaptive Typographic Prompt Injection Attacks against Black-box Multimodal Agents
arXiv cs.AIarXiv:2510.04257v2 Announce Type: replace-cross
Abstract: Multimodal agents built on large vision-language models are increasingly deployed in open-world settings but remain highly vulnerable to prompt injection. Typographic text-in-image attacks such as FigStep have been evaluated mainly on standalone vision-language models. For agents, user goals, webpage context, and action history make the effect of embedded instructions uncertain, and robustness to behavior-guided, adaptive visual injections remains insufficiently studied. We introduce AgentTypo, a black-box red-teaming framework that launches adaptive typographic prompt injection by embedding optimized text into webpage images. We formulate an attention-guided automatic typographic prompt-injection objective that favors the attacker's expected answer over the clean-page answer, together with task-conditioned visual attention from small question-answering models. A novel black-box optimization method searches text position, contrast, and color. To further strengthen the attack, we develop a multi-LLM system that iteratively refines injection prompts using evaluation feedback and retrieves successful past examples for strategy learning. Experiments on the VWA-Adv and Mind2Web benchmarks show that AgentTypo significantly outperforms state-of-the-art image-based prompt injection attacks. Across seven closed- and open-source model backends, AgentTypo-pro achieves 45%-83% ASR, outperforming the CLIP-based AgentAttack by 29-47% and previous typographic attacks by 25%-37% on VWA-Adv.
Towards General Continuous Memory for Vision-Language Models
arXiv cs.AIarXiv:2505.17670v3 Announce Type: replace-cross
Abstract: Language models (LMs) and their extension, vision-language models (VLMs), have achieved remarkable performance across various tasks. However, they still struggle with complex reasoning tasks that require multimodal or multilingual real-world knowledge. To support such capabilities, an external memory system that can efficiently provide relevant multimodal information is essential. Existing approaches generally concatenate image and text tokens into a long sequence as memory, which, however, may drastically increase context length and even degrade performance. In contrast, we propose using continuous memory, a compact set of dense embeddings to more effectively and efficiently represent multimodal and multilingual knowledge. Our key insight is that a VLM can serve as its own continuous memory encoder. We empirically show that this design improves performance on complex multimodal reasoning tasks. Building on this, we introduce a data-efficient and parameter-efficient method to fine-tune the VLM into a memory encoder, requiring only 1.2% of the model's parameters and a small corpus of 15.6K self-synthesized samples. Our approach CoMEM utilizes VLM's original capabilities to encode arbitrary multimodal and multilingual knowledge into just 8 continuous embeddings. Since the inference-time VLM remains frozen, our memory module is plug-and-play and can be flexibly integrated as needed. Extensive experiments across eight multimodal reasoning benchmarks demonstrate the effectiveness of our approach.
Generative Learner for Distributional Causal Effects
arXiv cs.AIarXiv:2503.17894v5 Announce Type: replace-cross
Abstract: We propose a generative learner for estimating conditional average treatment effects and characterizing the full distribution of these effects. The learner takes the form of a multi-head feed-forward neural network with three jointly estimated subnetworks: the propensity score, the conditional quantile function of the baseline outcome, and the conditional quantile treatment effect. Each quantile-indexed subnetwork combines a covariate representation with cosine quantile embeddings through element-wise multiplication. We then recover the conditional average treatment effect as an integral over conditional quantile treatment effects. Under the classical causal assumptions within the Neyman--Rubin potential outcomes framework, we find that the proposed generative learner reduces out-of-sample mean squared error relative to the generalized random forest, double machine learning, and generative adversarial networks, with gains ranging from 5.4% to 93.5% on average across experimental designs. In an empirical application, we formalize the Stefan--Boltzmann law within a unidirectional causal model and apply the method to publicly available stellar data. The estimated effects satisfy the restrictions the law implies.
ZenBrain: A Neuroscience-Inspired 7-Layer Memory Architecture for Autonomous AI Systems
arXiv cs.AIarXiv:2604.23878v4 Announce Type: replace
Abstract: ZenBrain is a seven-layer, neuroscience-derived architecture for agent memory in LLM agents. An open library holds the seven layers, a MemoryCoordinator and nine foundational algorithms; six Predictive Memory Architecture components run in a private production system. In the open library at version 0.5.1, decay acts only on working memory and nothing is replayed.
Ablating one component at a time exposes an effect we call cooperative masking. The ablation runs in a simulation harness that switches fifteen components as stand-ins at simulated values, not as production code. Under moderate load, fourteen of the fifteen ablations look costless. Raising decay, the horizon, the corpus and the queries, with no change to the components, moves ten of the fifteen in the same direction on all ten seeds - nine losses (Delta-Q down to -93.7%) and one gain. With ten seeds this is the one p-value the normal approximation of the Wilcoxon test can reach (5.06e-3), and it does not pass the Bonferroni-corrected level for fifteen contrasts (3.33e-3).
On LongMemEval_S (500 questions), the evaluated ZenBrain arm - a Python port of a scoring class from our test harness, not the seven-layer system - answers 47.7% of the questions correctly under a binary judge (95% interval [43.3, 52.1]). With the compared systems' adapters repaired, Letta reaches 48.4% and Mem0 47.2%. ZenBrain reaches 91.3% of a full-context baseline's accuracy with 1/109.7 of its input tokens in the answer step; building the memory is not counted. Of nine head-to-head comparisons of the retrieved context (3 competitors x 3 LLM judges), three hold at Bonferroni-corrected significance, all against A-Mem, and the remaining six show no significant difference.
On LoCoMo, the routing results of version 8 came from placeholder embeddings; with text-embedding-3-small, the flat variant (F1 0.059) lies above the full system (0.058).
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