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Received — 4 April 2026 ⏭

No Attacker Needed: Unintentional Cross-User Contamination in Shared-State LLM Agents

✇cs.AI, q-bio.NC updates on arXiv.org
By: Tiankai Yang · Jiate Li · Yi Nian · Shen Dong · Ruiyao Xu · Ryan Rossi · Kaize Ding · Yue Zhao
3 April 2026 at 12:00
arXiv:2604.01350v1 Announce Type: cross Abstract: LLM-based agents increasingly operate across repeated sessions, maintaining task states to ensure continuity. In many deployments, a single agent serves multiple users within a team or organization, reusing a shared knowledge layer across user identities. This shared persistence expands the failure surface: information that is locally valid for one user can silently degrade another user's outcome when the agent reapplies it without regard for scope. We refer to this failure mode as unintentional cross-user contamination (UCC). Unlike adversarial memory poisoning, UCC requires no attacker; it arises from benign interactions whose scope-bound artifacts persist and are later misapplied. We formalize UCC through a controlled evaluation protocol, introduce a taxonomy of three contamination types, and evaluate the problem in two shared-state mechanisms. Under raw shared state, benign interactions alone produce contamination rates of 57--71%. A write-time sanitization is effective when shared state is conversational, but leaves substantial residual risk when shared state includes executable artifacts, with contamination often manifesting as silent wrong answers. These results indicate that shared-state agents need artifact-level defenses beyond text-level sanitization to prevent silent cross-user failures.
Received — 26 March 2026 ⏭

PersonalQ: Select, Quantize, and Serve Personalized Diffusion Models for Efficient Inference

✇cs.AI, q-bio.NC updates on arXiv.org
By: Qirui Wang · Qi Guo · Yiding Sun · Junkai Yang · Dongxu Zhang · Shanmin Pang · Qing Guo
25 March 2026 at 12:00
arXiv:2603.22943v1 Announce Type: new Abstract: Personalized text-to-image generation lets users fine-tune diffusion models into repositories of concept-specific checkpoints, but serving these repositories efficiently is difficult for two reasons: natural-language requests are often ambiguous and can be misrouted to visually similar checkpoints, and standard post-training quantization can distort the fragile representations that encode personalized concepts. We present PersonalQ, a unified framework that connects checkpoint selection and quantization through a shared signal -- the checkpoint's trigger token. Check-in performs intent-aligned selection by combining intent-aware hybrid retrieval with LLM-based reranking over checkpoint context and asks a brief clarification question only when multiple intents remain plausible; it then rewrites the prompt by inserting the selected checkpoint's canonical trigger. Complementing this, Trigger-Aware Quantization (TAQ) applies trigger-aware mixed precision in cross-attention, preserving trigger-conditioned key/value rows (and their attention weights) while aggressively quantizing the remaining pathways for memory-efficient inference. Experiments show that PersonalQ improves intent alignment over retrieval and reranking baselines, while TAQ consistently offers a stronger compression-quality trade-off than prior diffusion PTQ methods, enabling scalable serving of personalized checkpoints without sacrificing fidelity.
Received — 14 March 2026 ⏭

Multi-Omics Characterization of Lactate-Associated Molecular Subtypes in Lung Cancer Suggests a Role for DKK1 in Lactate-Linked Migration, Invasion, and Lactylation Programs

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Hang Yu · Xiao-Bin An · Jin-Cheng Xu · Zhen Zhang · Long-Kai Yang · Long Qin · Qing-Sui Li · Chen-Hong Li · Xu Su · Dan Yang · Ning Wang · Jun-Nan Guo
14 March 2026 at 18:00

Cancers (Basel). 2026 Feb 25;18(5):735. doi: 10.3390/cancers18050735.

ABSTRACT

BACKGROUND: Lactate accumulation is increasingly recognized as a feature of tumor metabolic reprogramming that can coincide with immune dysregulation and aggressive phenotypes. The prognostic and immunologic relevance of lactate-associated heterogeneity in lung cancer remains to be clarified.

METHODS: We curated lactate-related genes and identified prognostic candidates in lung cancer cohorts. Consensus clustering was applied to define lactate-associated molecular subtypes, followed by characterization of survival and tumor microenvironment features. A LASSO-based gene signature was developed to generate an individual-level risk score and an integrated nomogram. Multi-omics analyses were used to evaluate concordance between transcriptomic and proteomic alterations. Single-cell transcriptomic data were analyzed to explore cellular heterogeneity in lactate-related programs. In vitro assays evaluated the response of candidate genes to lactate exposure and assessed cell migration and invasion under proliferation-inhibited conditions after genetic perturbation.

RESULTS: Two lactate-associated molecular subtypes were identified with distinct overall survival and divergent immune microenvironment features. Subtype 1 was associated with better outcomes and a more immune-inflamed profile, whereas Subtype 2 was associated with poorer outcomes and a myeloid-enriched, immunosuppressive contexture. Pathway analyses indicated subtype-associated differences in extracellular matrix-related processes and apoptosis-associated signaling. We developed an 11-gene prognostic signature and nomogram that stratified patients by risk across TCGA and GEO cohorts. Multi-omics integration highlighted ANLN, FGA, and DKK1 as consistently dysregulated at both transcript and protein levels. Among these candidates, DKK1 showed lactate-responsive induction in vitro. DKK1 perturbation altered lactate-enhanced migratory and invasive phenotypes and was accompanied by changes in intracellular lactate levels and global protein lactylation, supporting a potential feedforward relationship between lactate exposure, DKK1 expression, and lactylation.

CONCLUSIONS: This study characterizes lactate-associated molecular heterogeneity in lung cancer and provides a lactate-related subtype framework and prognostic risk model for patient stratification. The findings nominate DKK1 as a lactate-responsive candidate linked to migration/invasion phenotypes and lactate/lactylation changes in vitro.

PMID:41827671 | PMC:PMC12985219 | DOI:10.3390/cancers18050735

Multi-Omics Characterization of Lactate-Associated Molecular Subtypes in Lung Cancer Suggests a Role for DKK1 in Lactate-Linked Migration, Invasion, and Lactylation Programs

✇Omics In Lung
By: Hang Yu · Xiao-Bin An · Jin-Cheng Xu · Zhen Zhang · Long-Kai Yang · Long Qin · Qing-Sui Li · Chen-Hong Li · Xu Su · Dan Yang · Ning Wang · Jun-Nan Guo
14 March 2026 at 18:00

Cancers (Basel). 2026 Feb 25;18(5):735. doi: 10.3390/cancers18050735.

ABSTRACT

BACKGROUND: Lactate accumulation is increasingly recognized as a feature of tumor metabolic reprogramming that can coincide with immune dysregulation and aggressive phenotypes. The prognostic and immunologic relevance of lactate-associated heterogeneity in lung cancer remains to be clarified.

METHODS: We curated lactate-related genes and identified prognostic candidates in lung cancer cohorts. Consensus clustering was applied to define lactate-associated molecular subtypes, followed by characterization of survival and tumor microenvironment features. A LASSO-based gene signature was developed to generate an individual-level risk score and an integrated nomogram. Multi-omics analyses were used to evaluate concordance between transcriptomic and proteomic alterations. Single-cell transcriptomic data were analyzed to explore cellular heterogeneity in lactate-related programs. In vitro assays evaluated the response of candidate genes to lactate exposure and assessed cell migration and invasion under proliferation-inhibited conditions after genetic perturbation.

RESULTS: Two lactate-associated molecular subtypes were identified with distinct overall survival and divergent immune microenvironment features. Subtype 1 was associated with better outcomes and a more immune-inflamed profile, whereas Subtype 2 was associated with poorer outcomes and a myeloid-enriched, immunosuppressive contexture. Pathway analyses indicated subtype-associated differences in extracellular matrix-related processes and apoptosis-associated signaling. We developed an 11-gene prognostic signature and nomogram that stratified patients by risk across TCGA and GEO cohorts. Multi-omics integration highlighted ANLN, FGA, and DKK1 as consistently dysregulated at both transcript and protein levels. Among these candidates, DKK1 showed lactate-responsive induction in vitro. DKK1 perturbation altered lactate-enhanced migratory and invasive phenotypes and was accompanied by changes in intracellular lactate levels and global protein lactylation, supporting a potential feedforward relationship between lactate exposure, DKK1 expression, and lactylation.

CONCLUSIONS: This study characterizes lactate-associated molecular heterogeneity in lung cancer and provides a lactate-related subtype framework and prognostic risk model for patient stratification. The findings nominate DKK1 as a lactate-responsive candidate linked to migration/invasion phenotypes and lactate/lactylation changes in vitro.

PMID:41827671 | PMC:PMC12985219 | DOI:10.3390/cancers18050735

CDRRM: Contrast-Driven Rubric Generation for Reliable and Interpretable Reward Modeling

✇cs.AI, q-bio.NC updates on arXiv.org
By: Dengcan Liu · Fengkai Yang · Xiaohan Wang · Shurui Yan · Jiajun Chai · Jiahao Li · Yikun Ban · Zhendong Mao · Wei Lin · Guojun Yin
10 March 2026 at 12:00
arXiv:2603.08035v1 Announce Type: new Abstract: Reward modeling is essential for aligning Large Language Models(LLMs) with human preferences, yet conventional reward models suffer from poor interpretability and heavy reliance on costly expert annotations. While recent rubric-based approaches enhance evaluation transparency, they lack systematic quality control, yielding noisy and redundant criteria, failing to mitigate persistent biases (e.g., verbosity, position) in LLM evaluators, and creating a scalability-reliability trade-off. To address these limitations, we propose CDRRM (Contrast-Driven Rubric Reward Model), a framework built on a novel Contrast-then-Synthesis paradigm for high-quality rubric generation and guided preference judgment. CDRRM first conducts multi-dimensional contrastive profiling on preference pairs to identify causal discriminative factors, then synthesizes these insights into compact, context-aware rubrics to guide preference judg- ments. Extensive experiments on three authoritative benchmarks (RewardBench, RMBench, RMB) demonstrate that CDRRM achieves state-of-the-art performance across diverse domains and effectively mitigates aforementioned evaluation biases. Notably, our approach delivers exceptional data efficiency: training the rubric generator on only 3k high-quality samples empowers a frozen pre-trained judge model to outperform fully fine-tuned baselines. This work offers a scalable, interpretable, and data-efficient path for reward modeling.
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