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FrontierChallenge: Evaluating Scientific Workflow Completion

arXiv:2608.24979v2 Announce Type: replace Abstract: Scientific agents increasingly analyze data, execute code, and produce research artifacts, yet most benchmarks emphasize final answers, isolated programs, or a single domain. We introduce FrontierChallenge, a cross-domain benchmark comprising 300 end-to-end scientific workflows. In this paper, we release and evaluate 97 of these tasks, spanning quantum chemistry, molecular dynamics, materials characterization, analytical chemistry, life science, and electrochemistry/environment. Each task provides fixed inputs and specifies a bundle of required scientific deliverables. We evaluate twelve frontier models with three agent scaffolds. Pass Rate measures the fraction of tasks satisfying the full-completion criterion, while Avg. Score captures partial progress. Each of the best-performing configurations completed only 20 of the 97 released tasks, yielding a Pass Rate of 20.6%. Partial progress translated especially poorly into complete delivery in analytical chemistry and electrochemistry/environment: Avg. Scores reached 87.6 and 94.9, but the highest Pass Rates were only 4% and 0%. Among non-passing Claude Code trajectories, 75.5% still ended with language claiming completion. Complementary HDS6 process scores correlate strongly with task outcomes, supporting FrontierChallenge as a benchmark of Heavy Duty Solver capabilities. These findings show that neither high partial scores nor confident claims of completion reliably indicate that a scientific task has been fully delivered, highlighting the need to evaluate end-to-end workflow execution and the completeness of scientific deliverables together.

Hydroxy-induced cobalt oxides for syngas to light olefins

Nature, Published online: 01 April 2026; doi:10.1038/s41586-026-10204-4

A set of hydroxy promoters physically mixed with cobalt oxides for syngas to light olefins conversion using Fischer–Tropsch synthesis is shown to boost catalyst performance as well as simplifying the process and increasing sustainability.

New perspectives in immunotherapy for hepatocellular carcinoma: Focusing on resistance mechanism, biomarker, and personalized treatment

29 March 2026 at 18:00

Crit Rev Oncol Hematol. 2026 Mar 27;222:105305. doi: 10.1016/j.critrevonc.2026.105305. Online ahead of print.

ABSTRACT

The management of hepatocellular carcinoma (HCC) faces substantial and evolving challenges, driven by its aggressive biology, drug resistance, and the clinical urgency to detect recurrence. The treatment paradigm has undergone a profound transformation, evolving from surgical interventions and molecular targeted agents to the current era dominated by immunotherapy. Immune checkpoint inhibitors, particularly when used in combination with anti-angiogenic drugs or as part of dual-checkpoint blockade regimens, have established a new first-line standard of treatment for advanced HCC, delivering unprecedented survival improvements. Despite this progress, significant obstacles remain, including primary and acquired resistance, variable patient responses, and notably reduced efficacy in specific etiological subgroups. This comprehensive review synthesizes the emerging modalities such as bispecific antibodies, adoptive cell therapies, and innovative rational combinations that integrate systemic immunotherapy with locoregional treatments or novel targeted agents. Furthermore, we delve into the critical search for predictive biomarkers, encompassing liquid biopsy and multi-omics approaches, and dissect the complex cellular and molecular mechanisms underlying therapeutic resistance within the immunosuppressive tumor microenvironment. Finally, we outline future translational directions, emphasizing the expansion of immunotherapy, the development of tailored strategies for therapy-resistant disease, and the imperative move towards a personalized, biomarker-driven treatment framework. This review provides a cohesive overview of the field and charts a roadmap for future research to overcome the current challenges in HCC immunotherapy.

PMID:41905572 | DOI:10.1016/j.critrevonc.2026.105305

Not All Tokens Are Created Equal: Query-Efficient Jailbreak Fuzzing for LLMs

arXiv:2603.23269v1 Announce Type: cross Abstract: Large Language Models(LLMs) are widely deployed, yet are vulnerable to jailbreak prompts that elicit policy-violating outputs. Although prior studies have uncovered these risks, they typically treat all tokens as equally important during prompt mutation, overlooking the varying contributions of individual tokens to triggering model refusals. Consequently, these attacks introduce substantial redundant searching under query-constrained scenarios, reducing attack efficiency and hindering comprehensive vulnerability assessment. In this work, we conduct a token-level analysis of refusal behavior and observe that token contributions are highly skewed rather than uniform. Moreover, we find strong cross-model consistency in refusal tendencies, enabling the use of a surrogate model to estimate token-level contributions to the target model's refusals. Motivated by these findings, we propose TriageFuzz, a token-aware jailbreak fuzzing framework that adapts the fuzz testing approach with a series of customized designs. TriageFuzz leverages a surrogate model to estimate the contribution of individual tokens to refusal behaviors, enabling the identification of sensitive regions within the prompt. Furthermore, it incorporates a refusal-guided evolutionary strategy that adaptively weights candidate prompts with a lightweight scorer to steer the evolution toward bypassing safety constraints. Extensive experiments on six open-source LLMs and three commercial APIs demonstrate that TriageFuzz achieves comparable attack success rates (ASR) with significantly reduced query costs. Notably, it attains a 90% ASR with over 70% fewer queries compared to baselines. Even under an extremely restrictive budget of 25 queries, TriageFuzz outperforms existing methods, improving ASR by 20-40%.

Spatial Omics in Gastrointestinal Oncology: Recent Advances, Therapeutic Insights, and Clinical Translation

J Cancer. 2026 Jan 30;17(3):515-523. doi: 10.7150/jca.127381. eCollection 2026.

ABSTRACT

Gastrointestinal (GI) cancers remain a leading cause of cancer-related morbidity and mortality worldwide, largely due to their molecular heterogeneity, complex tumor microenvironment (TME), and variable treatment responses. In recent years, the emergence of spatially resolved omics technologies-encompassing spatial transcriptomics, proteomics, metabolomics, and epigenomics-has revolutionized the ability to interrogate tumor architecture with unprecedented resolution. These methods enable precise mapping of cellular and molecular interactions within intact tissue contexts, thereby uncovering spatially defined niches that influence tumor progression, immune evasion, and therapeutic resistance. In GI malignancies such as colorectal, gastric, and esophageal cancers, spatial omics have provided critical insights into cancer-stromal-immune crosstalk, identified predictive biomarkers for immunotherapy and targeted agents, and guided the development of novel therapeutic strategies. This review synthesizes the latest advances in spatial omics applied to GI oncology over the past five years, with an emphasis on their integration into early diagnosis, treatment stratification, and real-time monitoring of therapeutic efficacy. We also discuss current challenges, including standardization, data integration, and clinical validation, as well as future directions for incorporating spatial profiling into routine oncology practice. By bridging the gap between bench discoveries and bedside applications, spatial omics hold transformative potential for achieving truly personalized treatment in gastrointestinal cancers.

PMID:41869445 | PMC:PMC13003551 | DOI:10.7150/jca.127381

ELLA: Generative AI-Powered Social Robots for Early Language Development at Home

arXiv:2603.12508v1 Announce Type: cross Abstract: Early language development shapes children's later literacy and learning, yet many families have limited access to scalable, high-quality support at home. Recent advances in generative AI make it possible for social robots to move beyond scripted interactions and engage children in adaptive, conversational activities, but it remains unclear how to design such systems for pre-schoolers and how children engage with them over time in the home. We present ELLA (Early Language Learning Agent), an autonomous, generative AI-powered social robot that supports early language development through interactive storytelling, parent-selected language targets, and scaffolded dialogue. Using a multi-phased, human-centered process, we interviewed parents (n=7) and educators (n=5) and iteratively refined ELLA through twelve in-home design workshops. We then deployed ELLA with ten children for eight days. We report design insights from in-home workshops, characterize children's engagement and behaviors during deployment, and distill design implications for generative AI-powered social robots supporting early language learning at home.

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

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

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

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