AI News Archive: June 15, 2026 — Part 10
Sourced from 500+ daily AI sources, scored by relevance.
- Benchmarking LLM Agents on Meta-Analysis Articles from Nature Portfolio
Meta-analysis is a demanding form of evidence synthesis that combines literature retrieval, PI/ECO-guided study selection, and statistical aggregation. Its structured, verifiable workflow makes it an ideal substrate for evaluating systematic scientific reasoning, yet existing benchmarks lack ground ...
- Harmonizing Semantic and Collaborative in LLMs: Reasoning-based Embedding Generator for Sequential Recommendation
Sequential Recommender Systems (SRS) predict the next item of interest based on users' interaction histories and have been widely deployed, but hindered by long-tail problem. Large Language Models (LLMs), with strong semantic understanding and reasoning capabilities, offer a promising way to enrich ...
- RL-Index: Reinforcement Learning for Retrieval Index Reasoning
Retrieving external knowledge is essential for solving real-world tasks, yet it remains challenging when the relationship between a query and its relevant knowledge involves implicit and complex reasoning beyond surface-level semantic or lexical matching (e.g., mathematical problems relying on the s...
- How Much Do Reviews Really Contribute? A Study on Text-Enriched Matrix Factorization for Recommendations
Incorporating textual reviews into a Recommender System has become a prominent strategy for enriching collaborative signals with semantic information. However, the actual contribution of review-derived representations remains an open question, particularly when strong collaborative baselines are emp...
- OneRank: Unified Transformer-Native Ranking Architecture for Multi-Task Recommendation
Multi-task learning (MTL) is essential in recommender systems to enable complementary learning among diverse user feedback. While modern industrial practices have shifted from DNNs to Transformer-centric architectures to strengthen sequence modeling and scaling capacity, they still decouple feature ...
- PIANO: Personalized Reranking via Information Aggregation Node for Music Search Optimization
Unlike short-video content, music tracks have long lifecycles and lasting value. Effective music search re-ranking must therefore align the user's current query with long-term preferences while jointly optimizing Click-Through Rate (CTR) and Conversion Rate (CVR). However, existing methods suffer fr...
- Leveraging Code-Mixed Product Metadata and User Feedback for Personalized Recommendation on Daraz Bangladesh
Bangladeshi e-commerce platforms host millions of product reviews written in Bengali Unicode, English, and Banglish, where Bengali is phonetically transcribed in Latin script. However, the impact of code-mixed reviews on recommendation performance remains largely unexplored. We present the first suc...
- Climbing-fiber-like online readout adaptation in frozen continuous-time networks reproduces force-field adaptation and after-effects
Robotic motor control built on liquid neural networks and related continuous-time models, such as LTC and CfC, is typically trained offline via backpropagation through time and lacks an explicit mechanism for recalibrating online as plant dynamics change. We ask whether a frozen CfC core, whose liquid state spans a fixed continuous-time basis, can support cerebellar-style online adaptation by adapting only its linear readout with a climbing-fiber-like error signal. In a planar two-link reaching simulation with a velocity-dependent curl force field, we adapt the readout online with a feedback-error-learning (FEL) signal under a least-mean-squares (LMS) rule, leaving the core untouched. The frozen-core readout-only controller re-straightens curl-perturbed reaches and, upon field removal, produces a mirror-image after-effect---the signature of internal-model learning---that a feedback-only controller does not produce. The result generalizes from a dense CfC to a sparse Neural-Circuit-Policy (NCP) wiring when the recurrent state, rather than the projected motor output, is used as the readout basis; it is robust to force-field strength and direction; and a recursive-least-squares variant adapts faster but de-adapts slowly because its covariance collapses, a rigidity that a covariance-reset safe-forgetting rule removes. Within the explored two-link planar simulation range, we did not find a capacity limit that would require adapting the frozen core in the tested conditions. In this simulation study, adapting only the readout therefore provides a biologically inspired, low-cost online error-adaptation layer for offline-trained continuous-time controllers.
- VrySure: A Multi-Task AI Scientific Fraud Detection Platform for Identifying Manipulated and AI-Generated Biomedical Research Images
Integrity of scientific data is critical in biomedical research, where images often serve as primary evidence for experimental observations and conclusions. Advances in image-editing technologies and generative artificial intelligence (AI) have increased the accessibility and realism of visual manipulation, making detection through manual review increasingly challenging. To empower our laboratory researchers to continuously monitor and uphold scientific rigor and data integrity, and serve the global scientific community, we developed VrySure, an easy-to-deploy, AI-driven multi-task platform for automated image-integrity screening in biomedical research. VrySure integrates four detection modules: cross-image transformation detection, within-image copy-move detection, splicing detection in blot and gel images, and AI-generated image detection. The system identifies potentially manipulated images and, when possible, localizes suspicious regions using bounding-box outputs to support downstream verification. To support development and evaluation, we constructed task-specific datasets by combining public biomedical image resources, curated manipulated examples, and synthetic images generated by multiple generative AI systems. We evaluated VrySure using region-level F1 score, recall, precision, false negative rate (FNR), and false discovery rate (FDR) across multiple manipulation categories and compared its performance with two commonly used commercial image-integrity screening platforms under a predefined benchmark protocol. Under the tested conditions, VrySure achieved a higher F1 score and recall, lower FNR, and maintained a low FDR for within-image copy-move detection, splicing detection, and AI-generated image detection, while showing comparable performance in transformation detection. Beyond automated screening, VrySure is designed to support source-data comparison and evidence-based assessment in scientific integrity investigations. By integrating multiple detection capabilities into a unified and scalable workflow, VrySure provides a practical framework to improve the efficiency and consistency of image-integrity screening in biomedical research.
- RepGene: Toward a Unified Gene Representation Space Robust to Missing Biological Views
Genes can be described through multiple heterogeneous biological views, including genomic sequence, transcript sequence, protein sequence, textual knowledge, and single-cell expression context, yet existing gene embeddings remain largely modality-specific and difficult to compare or reuse when many views are unavailable. We study a narrower but practically important question: whether pretrained embeddings from these distinct sources can be organized into a shared gene representation interface that remains usable under severe missing-modality conditions. To investigate this question, we introduce RepGene, a lightweight single-branch framework that combines modality adapters, a shared encoder, presence-aware fusion, and self-supervised cross-view objectives to map five biological views into one latent space. Our goal is not to claim a new multimodal learning principle or to establish superiority over all simpler fusion strategies, but to provide an initial technical instantiation for testing whether such a shared interface is feasible in a fixed-feature setting. Under a two-stage protocol in which RepGene is trained self-supervised on frozen upstream embeddings and evaluated by downstream linear probing, we find preliminary evidence that the learned representation is broadly competitive in the full-modality setting and remains informative when only partial modality subsets are observed at inference time. The strongest signal in our study is robustness under missing views: average performance changes are often limited when one modality is removed, and even single-view inference remains non-trivial in the evaluated benchmark regime.These results do not resolve unified biological representation learning, and they should be interpreted in light of incomplete simple-fusion baselines, limited architectural ablation, benchmark dependence, and possible upstream feature exposure. We therefore position RepGene as a feasibility study and a starting point for stronger comparisons, broader benchmarks, and leakage-aware validation.
- Multiple Fault Analysis and Drug Therapy on Signaling Pathways Using Dynamic Bayesian Network-based Model
Cell growth is an intricate biological phenomenon that is closely regulated by the interplay between various growth factors and transcription factors. Signaling pathways are the main mediators in this event, which provide the driving force for mitosis or sometimes meiosis. However, when malfunctions occur within the biological network, they can cause uncontrolled cell division, regardless of external stimuli. By employing Dynamic Bayesian Networks (DBNs), these malfunctions can be explicitly simulated, offering insights into their effects on cellular behavior and growth regulation. To a significant extent, the resultant outcomes can be mitigated through the use of reduced drug combinations. This study delves into the intricacies of signaling pathway behavior under the influence of concurrent malfunctions. Initially, we replicate the effects of these dysfunctions within DBNs. Subsequently, drug therapy is applied to alleviate their impact. Our methodology introduces a parameter known as efficiency_score, enabling the identification of optimized drug combinations without prior knowledge of specific dysfunctions. Particularly relevant in the context of realistic cancer conditions, these tailored drug inhibition points demonstrate enhanced efficacy compared to conventional treatments. Leveraging GPU acceleration throughout the modeling process accelerates the analysis of multiple faults within the biological networks, rendering our approach notably faster and more efficient.
- Prediction of fMRI activity using vector autoregressive models: a comparison of sparse and low-rank approaches
Vector autoregressive (VAR) models have a history of being used to examine functional connectivity in the brain, as captured by functional MRI studies. Such models allow for an estimation of Granger-causal relationships between regions of interest across the brain. Unfortunately, since the number of parameters in the VAR model scales as the square of the number of regions, and this is typically large compared to the number of temporal observations, these parameter estimates will exhibit high variance. To address this challenge, we introduce a low-rank pre-smoothing method that applies a low-rank approximation to the observations before fitting a VAR model. We estimate these models using individual subject data from both task-based and resting-state conditions, tuning hyper-parameters at the population level. Our low-rank approach is directly compared against sparse and unconstrained estimation methods. Evaluations of predictive performance and model structure reveal that our pre-smoothing technique enables robust individual-level parameter estimation and significantly reduces prediction error, a finding further validated by synthetic experiments where the ground-truth parameters are known.
- Validation of Dynamic Bayesian Optimization for Human-in-the-Loop Optimization of Exoskeleton Control at User-Driven Walking Speed
Human-in-the-loop optimization (HILO) is an established method for identifying subject-specific optimal controllers for performance augmentation. For HILO algorithms to be useful in rehabilitation, however, the optimization algorithm may need to account for how the human response changes over time in response to assistance. In this study, we tested a modified version of Bayesian optimization (BO), dynamic Bayesian optimization (DBO), in a three-parameter optimization problem that sought to identify participant-specific optimal solutions for increasing walking speed. As opposed to BO, DBO accounts for the non-stationarity of human responses. Sixteen healthy participants received bilateral hip torque pulses delivered by a hip exoskeleton. The exoskeleton torque parameters were determined using HILO with either DBO or BO. Validation iterations were introduced to objectively compare performance across optimizers at different time points of HILO. The results showed that both DBO and BO significantly increased walking speed compared to baseline. When comparing performance between DBO and BO, DBO emerged as an improvement over BO both in terms of efficacy, modeling accuracy, and personalization. DBO induced changes in walking speed relative to baseline that exceeded those induced by BO at three of the four validation iterations. DBO outperformed BO in modeling accuracy in later validation iterations. DBO personalization induced changes in walking speed that were significantly greater than those induced by previously identified assistive solutions, while this was not the case of BO. Overall, our results indicate that DBO outperformed BO due to its greater ability to account for non-stationary aspects of the human response.
- Non-invasive intracranial pressure waveform reconstruction with deep learning
Purpose: Continuous intracranial pressure (ICP) monitoring requires invasive instrumentation, reaching only a narrow subset of critically ill patients. We tested whether deep learning models trained on routinely acquired extracranial signals can reconstruct continuous ICP waveforms at clinically relevant accuracy in an independent external cohort. Methods: In adults admitted to the ICU at a single quaternary health system, five deep learning architectures were trained on high-frequency arterial blood pressure (ABP), photoplethysmography (PPG), and electrocardiography (ECG) waveforms, using invasive (intraparenchymal) ICP as ground truth. Two fusion strategies (early and late) and three training objectives (waveform-morphology, baseline robust regression, and weighted robust regression) were evaluated. Models were externally validated on the held-out MIMIC-III Waveform Database. Performance was assessed by mean absolute error (MAE) and waveform similarity by Pearson correlation (r). Results: We analyzed data from 158 critically ill adults (~5,322 hours) across two quaternary health systems (Johns Hopkins Hospital, Baltimore; Beth Israel Deaconess Medical Center, Boston). Validation MAE ranged from 4.276 mmHg [95% CI 4.269, 4.283] (gated recurrent, late fusion) to 4.946 mmHg [95% CI 4.938, 4.956] (attention-based, early fusion), with Pearson r ranging from 0.599 [95% CI 0.599, 0.600] to 0.722 [95% CI 0.722, 0.723]. The multiscale encoder-decoder model demonstrated the most favorable MAE-correlation tradeoff. Conclusion: This is the first demonstration that continuous ICP waveform reconstruction from bedside signals generalizes across institutions at clinically relevant accuracy, establishing a foundation for non-invasive ICP monitoring and motivating validation across broader populations and ICP ranges.
- Entity-Aware Generation of Synthetic Clinical Progress Notes for Prostate Cancer using Large Language Model
Objectives: This study investigates large language models (LLMs) for clinical entity projection across substantial textual transformation. Specifically, we evaluate whether entities annotated in Spanish prostate cancer case reports can be preserved and explicitly projected when the source narratives are transformed into hospital-style clinical progress notes. Entity projection is treated as a generation-driven task, allowing paraphrase, condensation and narrative reorganisation, providing that clinically relevant entities remain recoverable as structured annotations. Methods: A corpus of 109 Spanish prostate cancer case reports was annotated using a silver-standard pipeline combining Spanish biomedical named-entity recognition with rule-based prostate-specific antigen (PSA) and Gleason extractors. The resulting silver-standard annotations were validated on a subset of generated notes against a gold-standard consensus produced by medical experts in prostate cancer. Four LLMs were evaluated for note generation and entity projection: GPT-5.4 Nano, Qwen 3.5:35B-A3B, GLM5 and Claude Sonnet 4.6. Entity-to-Entity (E2E) generation used XML-annotated cases as RAG-supported input, whereas Text-to-Entity (T2E) generation required models to generate and annotate notes directly from plain text cases. Zero-shot and few-shot prompting were tested. Projection quality was measured using precision, recall and F1-score, and complemented by LLM-as-a-judge evaluation using Kimi K2.6. Results: E2E consistently outperformed T2E, indicating that explicit entity-enriched in- put substantially facilitates entity preservation and localisation. GLM5 achieved the best E2E zero-shot result (F1 = 0.915), followed by Claude Sonnet 4.6 (F1 = 0.896). In T2E, few-shot prompting improved performance, with Claude Sonnet 4.6 reaching the highest score (F1 =0.718). Age, Gleason, Disease, Procedure, Duration and negation-related entities were robustly projected, whereas PSA and Dose showed less stable behaviour. Conclusion: LLMs can generate clinically plausible synthetic prostate cancer evolution notes while preserving a substantial proportion of source entities, particularly when explicit semantic annotations are provided as input. However, the lower and more variable performance observed in T2E highlights the difficulty of jointly generating clinical narratives and projecting entities without source-side information, especially for numerical and measure-related entities.
- Semantic Embeddings and the Peripheral Transcriptome in Ischemic Stroke: Connecting Molecular Signatures to NANDA-I Diagnoses
Objective: To construct and evaluate, in an exploratory manner, a pathophysiologic rationale link- ing biological pathways derived from the peripheral transcriptome in ischemic stroke (IS) to nursing diagnoses in the NANDA-I 2024-2026 taxonomy, while emphasizing that this association is not di- rect, deterministic, or automatically inferable from textual similarity with large language models (LLMs). Methods: A computational study was conducted using public secondary data from the Gene Ex- pression Omnibus series GSE16561, which includes 63 peripheral blood samples: 39 from indi- viduals with IS and 24 from healthy controls. The pipeline integrated transcriptomic analysis and functional enrichment, semantic mapping through ClinicalBERT embeddings, and mechanistic and clinical-conceptual judgment using Claude Sonnet 4.6 as a judge. The judgment stage was treated as the central interpretive layer, designed to mediate the transcriptome, pathophysiology, functional manifestation, and NANDA-I diagnosis. Results: The analysis identified a bimodal transcriptomic pattern, with activation of pathways re- lated to innate immunity and suppression of pathways related to adaptive immunity. Semantic map- ping generated 158 pathway-diagnosis pairs. The Spearman correlation between cosine similarity and the mechanistic score was negative and statistically significant (rho = -0.243; p = 2.09e-03), but weak in magnitude. This effect size indicates that semantic similarity explained less than 6% of the variance in mechanistic plausibility, reinforcing the insufficiency of embeddings as a stand- alone criterion. Of the 158 pairs, 14 were classified as high concordance, 8 as moderate, and 136 as divergent. Conclusion: The main value of this study lies in demonstrating that translating biological pathways into nursing diagnoses requires pathophysiologic, functional, and clinical-conceptual mediation. The prioritized pairs represent mechanistically plausible hypotheses for future research, without implying causality, direct clinical confirmation, or immediate care recommendations.
- ICD-10 Code Ambiguity Obscures Treatment-Eligible Adults with Spinal Muscular Atrophy: A Single-Center Chart Review and Patient Outreach Study
Background. Three disease-modifying therapies (DMTs) for spinal muscular atrophy (SMA) have been approved since 2016, yet many adults remain untreated. Identifying them depends on ICD-10 codes that capture SMA but do not reliably distinguish it from other related conditions. We examined, in one U.S. health system, both patients' engagement with therapy and the accuracy of the codes used to find them. Methods. We conducted a retrospective chart review of adults in an academic health system identified by SMA-associated ICD-10 codes, with manual adjudication of diagnosis and DMT status. Confirmed SMA-positive, DMT-naive patients were invited to a structured telephone interview on treatment awareness and barriers. Results. Of 60 charts, 22 (36.7%; 95% CI 25.6-49.3%) were appropriately coded for SMA or a related disorder; only 16 (26.7%) had molecularly confirmed SMA. The other 38 (63.3%) were miscoded, spanning spinal and bulbar muscular atrophy, asymptomatic carriers, prenatal screening, and conditions unrelated to SMA. Ten of the 16 confirmed patients (62.5%) were DMT-naive; one was interviewed, one declined, and eight could not be reached. The non-response is itself a finding: the patients least visible to administrative data are the hardest to reach. Conclusions. ICD-10 ambiguity is a barrier to treatment access in adult SMA, as is loss to follow-up. We make two recommendations: continuous documentation-coding alignment that uses natural language processing to verify the genetic precondition, and type-specific SMA codes (subcodes for Types 0-4) anchored on molecular SMN1 confirmation. Together these would support cohort identification, outreach, and evidence generation without adding to clinician burden.
- Artificial Intelligence-Based Detection of Airway Mucus Plugs on CT and Associations With Clinical Outcomes in COPDGene
RATIONALE: Airway mucus plugging is a clinically relevant manifestation of airway pathology in chronic obstructive pulmonary disease (COPD) and is associated with increased mortality even in early disease; however, visual computed tomography (CT) assessment is subjective and labor intensive. OBJECTIVES: To develop an AI-based quantitative CT method for automated detection of airway mucus plugging and evaluate associations with physiologic impairment and clinical outcomes. METHODS: Inspiratory CT scans from 8,971 COPDGene Phase 1 (GOLD 0-4 and PRISm) participants were analyzed. An AI-based framework combining 3D airway segmentation discontinuities and convolutional neural network classification identified mucus plug obstructions, yielding mucus plug burden (total plug count). Associations with outcomes were evaluated using covariate-adjusted models. MEASUREMENTS AND MAIN RESULTS : Higher mucus plug burden was associated with lower post-bronchodilator FEV % predicted ({rho} = -0.41; P < 0.001), greater air trapping (LAA < -856 HU; {rho} = 0.33; P < 0.001), worse health status (SGRQ; {rho} = 0.31; P < 0.001), and shorter 6-minute walk distance ({rho} = -0.26; P < 0.001). Among GOLD 1-4 participants, mucus plug presence was independently associated with increased all-cause mortality (adjusted hazard ratio, 1.28; P < 0.005) and exacerbation frequency (adjusted incidence rate ratio, 1.32; P < 0.005). Plug presence was also associated with increased respiratory mortality across GOLD categories and cardiovascular mortality in GOLD 1-2. CONCLUSIONS: AI-based quantitative CT assessment of airway mucus plugging provides a scalable, reproducible measure associated with physiologic impairment and adverse outcomes in COPD, supporting its role in risk stratification and future therapeutic studies.
- Automated AI-Based Ventricular Subcompartment Segmentation and Volumetry in Idiopathic Normal Pressure Hydrocephalus
Purpose In idiopathic normal pressure hydrocephalus (iNPH), longitudinal monitoring of ventricular size is important for diagnosis and treatment follow-up. This study aimed to validate a fully automated AI model for CT ventricular volumetry with subcompartments and to compare AI-derived volume changes with routine radiology assessments. Methods This retrospective, single-center study included 88 patients with iNPH and 456 non-contrast-enhanced head CT examinations. The model was trained on 38 manually labeled CT scans with 12 ventricular subcompartments. Outcomes included segmentation accuracy, correspondence between AI-derived longitudinal ventricular volume changes and radiology report categories (decreased, unchanged, increased), radiologist detection thresholds for ventricular change, and paired pre- and postoperative volume changes in 22 patients with ventriculoperitoneal shunt. Results Mean segmentation accuracy was high (Dice, 0.83). 91% of 100 segmentations were rated as excellent by an expert neuroradiologist. AI-derived ventricular volume changes corresponded well to radiology report categories (median total ventricular volume changes of -17% in cases reported as decreased, 0% in unchanged cases, and +22% in increased cases; all p < 0.001). Radiologists reported ventricular volume change in 50% of cases at an AI-measured relative volume change of +/-6%, and in 90% of cases at +21% for enlargement and -18% for decrease. After shunt placement, ventricular volume decreased by -8% (median), with the largest relative reductions observed in the right temporal and occipital horns. Conclusions Automated AI-based ventricular segmentation on CT enables accurate and reproducible assessment of ventricular volume changes in iNPH and complements routine radiological evaluation for longitudinal and postoperative monitoring.
- Trump’s Anthropic shutdown just made the case for non-American AI
At Washington's request, Anthropic suddenly took its newest and most powerful AI models offline over the weekend. The American company said it had little choice after the White House demanded it block access for all foreign nationals, including its own employees. Abroad, the incident offered a sobering reminder that the US not only dominates frontier […]
- Trump admin says Anthropic's 'recklessness' triggered export controls on latest AI models
Anthropic's Fable 5 AI model faced export controls after a senior administration official says the company's "recklessness" in addressing vulnerabilities damaged government trust.
- Anthropic scrambles to reverse AI ban after Amazon’s White House warning
Mythos 5 and Fable 5 models disabled due to ‘national security’ concerns
- Anthropic Block Marks US Reversal, Warning to Silicon Valley
The extraordinary move by the US to bar foreign access to Anthropic PBC’s best AI models underscores the Trump administration’s newfound willingness to exert control over a pivotal industry. It also reminds Silicon Valley that it’s working with an imperfectly …
- All the news about Anthropic’s new AI fight with the White House
Anthropic was already navigating one dispute with the government in its standoff with the Pentagon, and then came an order on June 12th to block off foreign access to its most recently released AI models, Fable 5 and Mythos 5. When they launched on June 9th, Anthropic said “Fable 5’s capabilities exceed those of any […]
- Anthropic Pulls Claude Fable and Mythos AI Models After Feds Claim Jailbreak
The dispute over an unconventional order raises questions about how the government will regulate advanced AI models.
- Anthropic Dispatches Staff to D.C., Racing to Resolve AI Export Restrictions
The startup is seeking a deal to end export restrictions that led to a shutdown of its most powerful AI models.
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A 60-second security score for any domain
- Facebook’s new AI Mode search gets its info from public posts
Your public Facebook posts could help inform AI-generated results in Meta's new AI Mode. When you search on Facebook, the "AI Mode" option will appear alongside the usual search modes like "People" and "Marketplace." It's one of several new AI features Meta is rolling out starting today, including photo presets that swap sports jerseys onto […]
- AI Firm Sarvam Turns Unicorn After USD 234M Series B Funding
AI Firm Sarvam Turns Unicorn After USD 234M Series B Funding india.entrepreneur.com
- Vorla AI
Vorla AI - Your ideas. Your AI Visual Studio.
- Salesforce to Buy AI Firm That Handles Customer Service
Salesforce Inc. has agreed to buy Fin, a firm that develops artificial intelligence-powered customer agents, for about $3.6 billion as the software company works to win new business for enterprise AI. Fin’s flagship product, AI Agent, handles customer queries via chat, email, WhatsApp, text message, phone and Slack. Bloomberg Intelligence Technology Analyst Anurag Rana joined Paul Sweeney and Scarlet Fu on "Bloomberg Intelligence." (Source: Bloomberg)
- Salesforce deepens AI automation push with $3.6 billion Fin buyout
Salesforce deepens AI automation push with $3.6 billion Fin buyout Reuters
- Salesforce to buy AI customer service platform Fin for $3.6 billion to boost agentic offerings
Businesses are accelerating their agentic offerings for enterprises as competition heats up.
- Salesforce to Acquire Customer AI Agent Fin for $3.6 Billion
Salesforce to Acquire Customer AI Agent Fin for $3.6 Billion The Information
- Salesforce to buy AI agent platform Fin for about $3.6 billion
Salesforce is acquiring Fin, an autonomous AI agent platform, for approximately $3.6 billion. This move will strengthen Salesforce's Agentforce platform. The acquisition aims to expand options for deploying AI agents in customer service. Customers will be able to integrate these agents with their existing systems. The deal is expected to conclude in the fourth quarter of Salesforce's fiscal year 2027.
- Salesforce Signs Definitive Agreement to Acquire Fin
Acquisition will bring Fin’s customer agent platform to companies of all sizes, accelerating time-to-value and expanding Salesforce’s ability to deliver autonomous agents across the enterprise SAN FRANCISCO, CA — June 15, 2026 — Salesforce (NYSE: CRM), the global leader in CRM, today announced it has signed a definitive agreement to acquire Fin, formerly Intercom, an […]
- Salesforce buying agentic AI firm Fin for $3.6 billion
When Salesforce announced on Monday its plan to buy agentic AI firm Fin, which, until a few weeks ago, was known as Intercom, it said that the company “resolves complex customer queries end-to-end, across every channel, including live chat, email, WhatsApp, SMS, phone, and Slack.” But analysts and consultants questioned whether the acquisition, one of more than a dozen Salesforce AI acquisitions since the beginning of last year, was going to end up accelerating the Salesforce functionality timeline or merely confuse matters. Scott Bickley , advisory fellow at Info-Tech Research, found the acquisition baffling. “I can’t figure out their focus. They are all over the place,” Bickley said. Bickley said making so many AI acquisitions in such a short timeframe means that there will be a lot of decisions about what stays and what doesn’t. “They are going to be integrating dozens of code bases in short order. When I zoom out, it is a little bit troubling.” “What they are trying to accomplish is not a bad goal: filling out their agent narrative. But they are trying to do it all at once. That means marketing will precede functionality,” Bickley said. For enterprise CIOs, he noted, “trying to map out the future will force a lot of questions. That tells me that they are trying to build the airplane while still in the air.” He noted that Salesforce would benefit from diversifying its installed base, because “its growth trajectory with enterprise is starting to wane”, and this acquisition would potentially help it increase its SMB market share. Fin claims 30,000 customers, which likely means most of them are SMBs. But that means Salesforce will have to make other changes, he said. “They will have to adjust. They cannot charge enterprise pricing with that model.” The news release about the announcement said that the acquisition “is expected to close in the fourth quarter of Salesforce’s fiscal year 2027” which spans the calendar dates from Nov. 1, 2026, through Jan. 31, 2027. The company declined a request for an interview about the deal. Fin had been known as Intercom for almost its corporate life, but it changed its name to Fin on May 12, just a few weeks before the acquisition announcement. Given that negotiations were almost certainly resolved by mid-May, as lawyers and marketers from both companies fine-tuned language for SEC filings and the news release, it seemed unusual to announce a corporate name change in mid-May, rather than wait for the acquisition to close and to then let the new owners decide. Justin Greis , CEO of consulting firm Acceligence, found the name change “fascinating,” but said his instinct is that the rebrand was driven by long-term positioning rather than short-term transaction considerations. “Founders and leadership teams typically spend months shaping the identity they believe best represents the future of the company,” he pointed out. “If they had already concluded that the market opportunity centered on Fin as a category-defining AI platform, changing course simply because acquisition discussions were underway may have felt shortsighted.” Needs a clear vision “For CIOs, implementation complexity has become one of the biggest barriers to AI adoption. The challenge is rarely access to models. It’s connecting data, workflows, governance, security, and business processes to deliver tangible results,” Greis said. “Enterprise AI is moving from a technology conversation to an execution conversation, and time-to-value is becoming the new battleground for competitive advantage. I think Salesforce is making a strategic bet that enterprises want multiple paths to adoption.” Greis added that enterprise CIOs already assume that product lines, especially those including generative AI and agentic AI, will constantly change. “What matters far more is whether Salesforce provides a clear vision for where customers should place their bets over the next three to five years and incentivize adoption, stickiness, and value to their customers,” he said. “Enterprise buyers can manage product change, but unclear roadmaps create much bigger problems than portfolio consolidation. No CIO would build their foundation on a platform that has a high risk of sunsetting in the next few years.” But, he added, enterprise CIOs today have little choice but to accept a lot of uncertainty from all of the major vendors. Others argued that CIOs still need some firm targets so that they can make concrete decisions for their enterprises. “The execution risk is acquisition indigestion, the same affliction that turns platform breadth into licensing fog,” said Sanchit Vir Gogia , chief analyst at Greyhound Research. “The most important date in this announcement is the one that does not exist: Salesforce has given a transaction close window but it has not given an integration timetable. Acquisitions run on three clocks. Ownership transfers fastest. Commercial alignment follows. Architectural integration, where identity, data and governance actually converge, runs slowest and decides the outcome. The credible path to meaningful convergence is 12 to 24 months beyond close, not a quarter beyond announcement.” Salesforce’s own history illustrates this, he said. A straightforward acquisition has closed inside two months, a complex one took the better part of a year, “and value realization trailed both,” Gogia said. A better way to pay for AI However, one of the potential benefits that Fin would bring to Salesforce is Fin’s differentiated pricing strategy. One of the aspects of AI that delivers the most problems for CIOs is the lack of any direct ROI . Fin has been trying to address that by charging only when a customer experiences an apparent good outcome. Nik Kale , a member of the Coalition for Secure AI (CoSAI) and of ACM’s AI Security (AISec) program committee, pointed out, “what Salesforce paid $3.6 billion for is an outcome-pricing model and a book of proof that the model works. Fin charges about 99 cents per resolved conversation and the customer pays nothing when the agent fails. That pricing structure is the actual asset because it sidesteps the exact thing that’s been gating agentic adoption, which is that enterprises don’t want to pay for agents they haven’t learned to trust yet.” But, Kale said, there is a catch. “For CIOs, the per-outcome model quietly moves the risk of a bad agent decision from the buyer back to the vendor, at least on paper. That sounds great until you ask who writes the definition of ‘resolved.’ In outcome pricing, a customer who gives up and walks away looks identical to a customer who actually got helped because both of them stop replying,” he said. “Silence gets billed as success. So outcome pricing looks like the vendor taking on your risk, but when the vendor also owns the definition of a win, you’ve handed them the scoreboard and the referee. The definition of ‘outcome’ is where the governance lives and, right now, it lives in the vendor’s measurement, not yours.” “Salesforce didn’t buy a better agent. They bought a better way to get paid for one,” he said.
- Salesforce Stock Rises as $3.6 Billion Deal for AI Company Eases Software Fears
Salesforce Stock Rises as $3.6 Billion Deal for AI Company Eases Software Fears Barron's
- Salesforce reels in customer support AI specialist Fin for $3.6B
Support bot maker claims its AI agents can resolve three-quarters of customer queries without human help
- India's HCLTech to buy 10.5% stake in Sarvam AI, valuing startup at $1.5 billion
India's HCLTech to buy 10.5% stake in Sarvam AI, valuing startup at $1.5 billion Reuters
- Sarvam raises $234 million led by HCLTech at $1.5 billion valuation
India's Sarvam AI has secured $234 million in a funding round led by HCLTech, valuing the homegrown AI startup at $1.5 billion. This investment fuels Sarvam's mission to develop sovereign AI solutions and large language models tailored for India's diverse languages and needs.
- Sarvam Enters Unicorn Club With $234 Mn Funding Round Led By HCLTech
AI startup Sarvam has become India’s 130th unicorn after raising $234 Mn (about ₹2,210 Cr) as part of its $300…
- Sarvam AI becomes unicorn after raising $234M as India doubles down on home-grown AI
Sarvam AI becomes unicorn after raising $234M as India doubles down on home-grown AI YourStory.com
- Sarvam AI becomes unicorn after raising $234 mn; HCLTech acquires over 10%
The sovereign AI startup raised $234 million in the first close of its Series B round, with HCLTech emerging as the lead strategic investor
- HCLTech invests ₹1,427 crore as a lead strategic investor in Sarvam AI
In total, the AI company has raised $234 million (around ₹2,218.61 crore) in the first close of its $300 million Series B at a postmoney valuation of $1.5 billion
- India: AI startup Sarvam raises $234m in first close of Series B at $1.5b valuation
India: AI startup Sarvam raises $234m in first close of Series B at $1.5b valuation DealStreetAsia