JAMSessions: Multi-Domain Frameworks

• JAMSessions is a multifaceted term representing a hybrid 5G anti-jamming framework, a music recommendation dataset, and a reproducible game-code workflow.
• The 5G implementation combines advanced DoA estimation, adaptive MVDR beamforming, and lightweight ML prediction to achieve high SNR and precise null steering.
• In music and game-code contexts, JAMSessions enables personalized retrieval via multimodal embeddings and deterministic pipelines for structured project-level analysis.

JAMSessions denotes several technically distinct constructs in recent research literature. Most prominently, the label is used for a hybrid anti-jamming framework for 5G mobile-jammer mitigation, for a dataset supporting personalized natural-language music recommendation, and for workflow patterns built from game jam artifacts in project-level code research. A plausible implication is that the term has become a compact marker for systems organized around either jamming, jam-session interaction, or game-jam-derived data, rather than a single standardized architecture or benchmark (Holguin et al., 12 May 2025, Melchiorre et al., 21 Jul 2025, Sun et al., 18 Jun 2026).

1. Scope and major usages

The supplied literature uses “JAMSessions” in at least three distinct senses. In wireless communications, it denotes a receiver-side framework that fuses subspace DoA estimation, adaptive beamforming, and lightweight prediction to mitigate mobile jammers in 5G. In recommender systems, it denotes a corpus of user–query–item triples for multimodal and personalized music retrieval. In project-level code research, the term appears at workflow level, where verified game jam projects are converted into deterministic training and evaluation pipelines through JAMER, JamSet, and JamBench (Holguin et al., 12 May 2025, Melchiorre et al., 21 Jul 2025, Sun et al., 18 Jun 2026).

Usage	Definition in the data	Representative source
5G anti-jamming	Hybrid spatial-signal-processing and machine-learning framework using MUSIC, MVDR, and DoA prediction	(Holguin et al., 12 May 2025)
Music recommendation	Dataset of user–query–item triples for JAM	(Melchiorre et al., 21 Jul 2025)
Game-jam workflow substrate	Workflow label associated with JAMER, JamSet, and JamBench on Godot	(Sun et al., 18 Jun 2026)

This multiplicity matters because the three usages share vocabulary but not objectives, data models, or evaluation criteria. One concerns contested radio links, one concerns personalized retrieval over multimodal embeddings, and one concerns deterministic project-level software verification. Treating them as interchangeable would obscure the underlying technical differences.

2. JAMSessions as a 5G anti-jamming framework

In the wireless-communications usage, JAMSessions is a hybrid spatial-signal-processing and machine-learning framework that mitigates mobile jammers in 5G by unifying high-resolution DoA estimation via MUSIC, adaptive nulling via MVDR, and short-horizon DoA prediction via a lightweight ML model. The system operates in real time at the receiver, either UE or gNB, continuously estimating interference directions, pre-steering nulls for mobile jammers, and preserving gain toward the desired 5G signal. In the reported highway mobility simulations, it achieved an average $\Delta \mathrm{SNR}$ of $9.58\ \mathrm{dB}$, a maximum of $11.08\ \mathrm{dB}$, and up to $99.8\%$ DoA estimation accuracy within a $\pm 1^\circ$ tolerance (Holguin et al., 12 May 2025).

The underlying signal model is a narrowband array-receive model. For an $M$-element ULA, the snapshot is

$x(t) = \sum_{k=1}^{K} a(\theta_k) s_k(t) + n(t),$

with steering vector

$$a(\theta) = [1, e^{-j (2\pi d/\lambda) \sin \theta}, \ldots, e^{-j (2\pi d/\lambda) (M-1) \sin \theta}]^T.$$

Covariance estimation uses

$$\hat{R}_x = \frac{1}{N} \sum_{t=1}^{N} x(t) x^H(t),$$

with diagonal loading $\hat{R}_x \leftarrow \hat{R}_x + \delta I$ when snapshot support is limited. Although the simulation platform used a $9.58\ \mathrm{dB}$0 URA, the ULA formulation underpins the theory and extends to URA by separable Kronecker steering across azimuth and elevation.

DoA extraction is performed with MUSIC through the pseudo-spectrum

$9.58\ \mathrm{dB}$1

The framework uses eigenspace decomposition of the covariance, covariance averaging, azimuth scanning from $9.58\ \mathrm{dB}$2 to $9.58\ \mathrm{dB}$3, and, when needed, forward–backward averaging and spatial smoothing to stabilize subspace estimation under coherent multipath. The reported accuracy metric is the fraction of snapshot-level azimuth estimates whose absolute error satisfies $9.58\ \mathrm{dB}$4.

Interference suppression is then handled by MVDR. For desired direction $9.58\ \mathrm{dB}$5, the beamformer solves

$9.58\ \mathrm{dB}$6

with closed form

$9.58\ \mathrm{dB}$7

The beam pattern

$9.58\ \mathrm{dB}$8

places deep nulls at jammer DoAs while maintaining the mainlobe toward the desired signal. The reported example beam pattern places a null near $9.58\ \mathrm{dB}$9 and a mainlobe near $11.08\ \mathrm{dB}$0.

The machine-learning component is deliberately lightweight. JAMSessions uses linear regression over the previous $11.08\ \mathrm{dB}$1 DoA values,

$11.08\ \mathrm{dB}$2

trained with

$11.08\ \mathrm{dB}$3

When MUSIC error exceeds a threshold, the predictor pre-steers MVDR by replacing $11.08\ \mathrm{dB}$4 with $11.08\ \mathrm{dB}$5. This reduces null-chasing under rapid motion or low SNR.

The reported processing flow is covariance estimation, eigen-decomposition and MUSIC scanning, peak selection, ML smoothing or forecasting when confidence is low, MVDR weight computation, and beamforming update. For small arrays, the measured per-collection runtimes were approximately $11.08\ \mathrm{dB}$6 for MUSIC, $11.08\ \mathrm{dB}$7 for MVDR, and $11.08\ \mathrm{dB}$8 for ML, yielding a reported total of $11.08\ \mathrm{dB}$9 for the MUSIC–MVDR–ML configuration, compared with $99.8\%$0 for MUSIC–MVDR and $99.8\%$1 for fixed beamforming. In the same comparison, the corresponding average $99.8\%$2 and DoA accuracy values were $99.8\%$3 and $99.8\%$4, $99.8\%$5 and $99.8\%$6, and $99.8\%$7 and $99.8\%$8.

Deployment is explicitly framed in 5G NR terms. The framework can use SSB and CSI-RS receptions to form $99.8\%$9, integrate predicted DoAs into beam management, exploit TDD reciprocity for downlink steering, and scale from small UE arrays to gNB massive MIMO. The data further states that activation is appropriate when interference is detected by elevated covariance power or degraded CQI, and that practical beam/null updates can run at a $\pm 1^\circ$0–$\pm 1^\circ$1 cadence depending on mobility. This suggests a design positioned between classical adaptive beamforming and beam-management-aware anti-jamming.

3. JAMSessions as a music-recommendation dataset

In recommender-systems research, JAMSessions is a real-world dataset of user–query–item triples built to train and evaluate JAM, “Just Ask for Music,” a lightweight framework for natural-language music recommendation. The dataset is designed to combine conversational natural-language queries, interpreted as short-term intent, with long-term user preferences and multimodal track representations. It contains $\pm 1^\circ$2 user–query–item triples, $\pm 1^\circ$3 unique users, and $\pm 1^\circ$4 unique tracks, collected over one week in March 2025 from Deezer search logs. The paper by Melchiorre, Epure, Masoudian, Escobedo, Hausberger, Moussallam, and Schedl presents it as a bridge across prior corpora that contain playlists without users, users without queries, or only small conversational sets (Melchiorre et al., 21 Jul 2025).

Each triple conceptually contains an anonymized user embedding reflecting long-term collaborative-filtering preferences, a short natural-language query text, and an anonymized track identifier with multimodal item embeddings. The item modalities are audio embeddings extracted via contrastive learning, lyrics embeddings from multilingual-e5-base applied to full lyrics, and collaborative-filtering item embeddings from matrix factorization of a recency- and frequency-weighted interaction matrix. User representation is a single CF embedding. Query embeddings in JAM are obtained with ModernBERT-base, while the dataset itself provides query text and precomputed user and item embeddings.

The curation pipeline is operationally specific. Each triple corresponds to a user who entered a search query, explored results, and landed on an editor-curated playlist listened to for over $\pm 1^\circ$5 minutes. Because raw search queries are often short and repetitive, playlist titles and descriptions are used to prompt DeepSeek-R1-Distill-Qwen-7B in a two-shot setup, capped at $\pm 1^\circ$6 English words, to generate augmented queries. The paper states that an internal quality check found most augmented queries accurate, with some errors. The resulting data is sparse: interactions per track are approximately $\pm 1^\circ$7, and interactions per user are approximately $\pm 1^\circ$8.

JAM itself models recommendation as translation in a shared latent space. The core assumption is

$\pm 1^\circ$9

with $M$0, and the implemented scoring function is

$M$1

All upstream embeddings are projected into a shared $M$2-dimensional space by one-layer feed-forward encoders. Multimodal aggregation is then handled either by simple averaging, by CrossMixing through query-conditioned cross-attention,

$M$3

or by sparse mixture-of-experts gating,

$M$4

Training uses BPR-style contrastive learning with negative sampling; the reported setup uses four negatives per positive.

The evaluation uses chronological splitting, with the last day as test and the previous day as validation, and reports Recall@10, Recall@100, NDCG@10, and NDCG@100. CrossMixing is the strongest JAM variant, with Recall@10 $M$5, Recall@100 $M$6, NDCG@10 $M$7, and NDCG@100 $M$8. These results exceed Random, Pop, TalkRec, TwoTower, AvgMixing, and the two tested MoEMixing settings. The paper also reports that CrossMixing consistently outperforms all baselines, while small-$M$9 sparse gating underperforms averaging and attention.

Several limitations are explicit. The anonymization method is unspecified; no formal privacy guarantee is stated; raw audio and raw lyrics are not released; the one-week collection window induces strong sparsity; coverage rates for modalities are not reported; and the combination of editor-curated playlists with a $x(t) = \sum_{k=1}^{K} a(\theta_k) s_k(t) + n(t),$0-minute listening threshold may bias the corpus toward sustained-listening and mainstream contexts. A common misconception corrected by the benchmark is that query semantics alone suffice: the reported TwoTower baseline, which retains long-term user modeling but no query modeling, underperforms TalkRec, which models multimodal retrieval without long-term user preferences, and both underperform JAM. The intended lesson is that long-term preference, short-term intent, and multimodal item structure must be aligned jointly.

4. JAMSessions workflows in project-level game-code research

In the game-engine literature, “JAMSessions” is not the dataset name but a workflow label associated with JAMER, which operationalizes the game jam tradition into a reproducible, large-scale, project-level code dataset and benchmark on Godot. JAMER constructs JamSet and JamBench from open-source game jam projects and is described as “precisely the substrate needed to power ‘JAMSessions’ research workflows.” The emphasis is on deterministic verification from repository discovery to runtime behavior logging, rather than on jam sessions as interactive performance (Sun et al., 18 Jun 2026).

The scale is substantial. Starting from approximately $x(t) = \sum_{k=1}^{K} a(\theta_k) s_k(t) + n(t),$1 candidate repositories, the pipeline identifies Godot $x(t) = \sum_{k=1}^{K} a(\theta_k) s_k(t) + n(t),$2 $x(t) = \sum_{k=1}^{K} a(\theta_k) s_k(t) + n(t),$3D projects, applies pre-filters including $x(t) = \sum_{k=1}^{K} a(\theta_k) s_k(t) + n(t),$4, $x(t) = \sum_{k=1}^{K} a(\theta_k) s_k(t) + n(t),$5, and removal of projects with more than $x(t) = \sum_{k=1}^{K} a(\theta_k) s_k(t) + n(t),$6 3D content, then executes a four-level deterministic verification pipeline. After L1 file integrity, L2 compilation correctness, and L3a runtime stability, $x(t) = \sum_{k=1}^{K} a(\theta_k) s_k(t) + n(t),$7 projects pass; after L3b deterministic runtime behavior collection, $x(t) = \sum_{k=1}^{K} a(\theta_k) s_k(t) + n(t),$8 projects remain, with $x(t) = \sum_{k=1}^{K} a(\theta_k) s_k(t) + n(t),$9 “silent” projects excluded. JamBench comprises $$a(\theta) = [1, e^{-j (2\pi d/\lambda) \sin \theta}, \ldots, e^{-j (2\pi d/\lambda) (M-1) \sin \theta}]^T.$$0 manually verified projects, while JamSet contains $$a(\theta) = [1, e^{-j (2\pi d/\lambda) \sin \theta}, \ldots, e^{-j (2\pi d/\lambda) (M-1) \sin \theta}]^T.$$1 projects converted into multi-turn training samples.

The deterministic pipeline is unusually detailed. L1 checks project integrity; L2 runs Godot headless compilation and looks for essential engineering patterns; L3a runs the project headlessly for $$a(\theta) = [1, e^{-j (2\pi d/\lambda) \sin \theta}, \ldots, e^{-j (2\pi d/\lambda) (M-1) \sin \theta}]^T.$$2 without inputs; and L3b generates a deterministic input strategy from offline-produced \texttt{eval_config.json}, then runs for $$a(\theta) = [1, e^{-j (2\pi d/\lambda) \sin \theta}, \ldots, e^{-j (2\pi d/\lambda) (M-1) \sin \theta}]^T.$$3 with a $$a(\theta) = [1, e^{-j (2\pi d/\lambda) \sin \theta}, \ldots, e^{-j (2\pi d/\lambda) (M-1) \sin \theta}]^T.$$4 menu bypass and $$a(\theta) = [1, e^{-j (2\pi d/\lambda) \sin \theta}, \ldots, e^{-j (2\pi d/\lambda) (M-1) \sin \theta}]^T.$$5 gameplay phase, logging positions, velocities, signals, score and health changes, node additions and removals, and scene transitions. The paper is explicit that no model calls occur during deterministic verification.

JamBench defines two task families. Task 1 is theme-driven generation, with a theme-only variant and a theme-plus-gameplay-description variant. Task 2 is multi-granularity completion over real projects: function-level restoration, script-level completion, and full-script re-implementation. Evaluation combines engine pass rates with Structural Completeness Score and Behavioral Alignment Score. The reported SCS is

$$a(\theta) = [1, e^{-j (2\pi d/\lambda) \sin \theta}, \ldots, e^{-j (2\pi d/\lambda) (M-1) \sin \theta}]^T.$$6

and BAS averages normalized runtime-similarity terms over numeric dimensions and signal-trigger overlap.

The benchmark’s central empirical result is a capability cliff with project scale. On Task 2a, average runtime pass rates drop from $$a(\theta) = [1, e^{-j (2\pi d/\lambda) \sin \theta}, \ldots, e^{-j (2\pi d/\lambda) (M-1) \sin \theta}]^T.$$7 on Small projects to $$a(\theta) = [1, e^{-j (2\pi d/\lambda) \sin \theta}, \ldots, e^{-j (2\pi d/\lambda) (M-1) \sin \theta}]^T.$$8 on Large ones. Task 1 has an average L3a pass rate of $$a(\theta) = [1, e^{-j (2\pi d/\lambda) \sin \theta}, \ldots, e^{-j (2\pi d/\lambda) (M-1) \sin \theta}]^T.$$9, but much lower SCS and BAS, indicating compilable yet structurally minimal outputs. Code agents improve compilation and L3a rates, often by tens of percentage points, but yield near-zero gains in SCS and BAS. JAMER interprets this as evidence that the bottleneck is architectural design rather than syntactic correctness. The same point recurs in the examples of minimal shells, engine-specific semantic failures, and cross-file semantic drift.

Within this literature, the phrase “JAMSessions” therefore refers less to a named artifact than to a reproducible research workflow grounded in real jam projects, deterministic engine verification, and behavior-aware evaluation. A plausible implication is that the jam session motif here is infrastructural: rapid, theme-driven, project-complete development is converted into a tractable substrate for studying code generation, repair, and planning at project scale.

5. Literal jam sessions in real-time music systems

A broader, literal sense of jam sessions appears in live human–AI music systems, although these papers do not present their core artifact under the exact name “JAMSessions.” StreamMUSE formulates live accompaniment as frame-synchronous streaming inference, while ReaLJam provides an anticipatory client–server interface and protocol for real-time human–AI jamming with a Transformer-based agent tuned by reinforcement learning. Taken together, they describe a technical regime in which jam sessions are treated as causal, synchronized, low-latency generation problems rather than as datasets or anti-jamming systems (Zheng et al., 10 Jun 2026, Scarlatos et al., 28 Feb 2025).

StreamMUSE defines the accompaniment problem causally:

$$\hat{R}_x = \frac{1}{N} \sum_{t=1}^{N} x(t) x^H(t),$$0

Its client continuously sends high-frequency inference requests aligned to a musical clock, while the server performs generation over a finite horizon. The system models round-trip latency as

$$\hat{R}_x = \frac{1}{N} \sum_{t=1}^{N} x(t) x^H(t),$$1

with

$$\hat{R}_x = \frac{1}{N} \sum_{t=1}^{N} x(t) x^H(t),$$2

and a heavy-tailed network term. Real-time feasibility is expressed by

$$\hat{R}_x = \frac{1}{N} \sum_{t=1}^{N} x(t) x^H(t),$$3

for safety and

$$\hat{R}_x = \frac{1}{N} \sum_{t=1}^{N} x(t) x^H(t),$$4

for playback continuity. The reported experiments show that real-time performance and music quality move together, and the paper states that local deployment can support very small $$\hat{R}_x = \frac{1}{N} \sum_{t=1}^{N} x(t) x^H(t),$$5 settings, whereas LAN and WAN settings require larger horizons to absorb latency tails.

ReaLJam focuses on anticipation and user-facing communication of plan. Time is quantized into $$\hat{R}_x = \frac{1}{N} \sum_{t=1}^{N} x(t) x^H(t),$$6-note frames; the client repeatedly sends full session history, currently scheduled future chords, generation start frame, and control settings such as BPM, lookahead, commit length, temperature, and silence period. The server returns a chord plan over the lookahead horizon. A commit period fixes near-future chords, while the adaptive period is re-generated continuously. The interface renders committed chords as opaque and uncommitted ones as semi-transparent. Most server responses return within $$\hat{R}_x = \frac{1}{N} \sum_{t=1}^{N} x(t) x^H(t),$$7, which the paper notes enables “single-frame round trips at $$\hat{R}_x = \frac{1}{N} \sum_{t=1}^{N} x(t) x^H(t),$$8 BPM.”

ReaLJam also reports a small user study with six experienced musicians. The overall post-study Likert means were $$\hat{R}_x = \frac{1}{N} \sum_{t=1}^{N} x(t) x^H(t),$$9 for musical interest, $\hat{R}_x \leftarrow \hat{R}_x + \delta I$0 for adaptation, $\hat{R}_x \leftarrow \hat{R}_x + \delta I$1 for control, $\hat{R}_x \leftarrow \hat{R}_x + \delta I$2 for enjoyment, and $\hat{R}_x \leftarrow \hat{R}_x + \delta I$3 for utilization. RL-tuned variants were preferred over the online pretrained model; the online model was strongly dispreferred on average. Qualitative feedback emphasized low latency, surprise, and ease of “just jamming away,” while also noting limited higher-level phrase structure.

This line of work clarifies that jam sessions in music-system research are not only a cultural metaphor. They are operationalized through frame quantization, causal conditioning, buffer overlap, request cadence, lookahead, commit policies, and explicit latency constraints. A common misconception corrected by these systems is that generation speed alone solves live accompaniment; both papers argue that timing alignment to an external clock is a separate systems problem.

6. Relation to broader jamming and anti-jamming research

The wireless-communications usage of JAMSessions sits inside a larger anti-jamming landscape. Measurement-driven adaptation in 802.11, exemplified by ARES, emphasizes carrier-sense-threshold tuning and memory-augmented rate control under intermittent jamming, achieving throughput improvements of up to $\hat{R}_x \leftarrow \hat{R}_x + \delta I$4 in a mesh with mobile frequent jammers (0906.3038). At MAC level, AntiJam proves $\hat{R}_x \leftarrow \hat{R}_x + \delta I$5-competitive throughput in single-hop networks under adaptive reactive jammers, while JADE provides analogous constant-competitive guarantees for sufficiently dense multi-hop unit-disk networks under adaptive adversarial jamming (Richa et al., 2010, Richa et al., 2010). These systems address access control and contention, rather than the array processing central to 5G JAMSessions.

Another branch is detector-centric. JSTS monitors joint space-time sparsity in mission-critical mMTC uplink access and performs sequential change detection on frame-level features, achieving reported $\hat{R}_x \leftarrow \hat{R}_x + \delta I$6 at $\hat{R}_x \leftarrow \hat{R}_x + \delta I$7 in the stated setting (Wang et al., 2023). SAJD transfers the adaptation problem to AI/ML-integrated O-RAN, using a closed loop of Labeler rApp, Training Manager rApp, and xApp over InfluxDB and ClearML to maintain high accuracy under dynamic ON/OFF interference scenes with changing power and noise amplitudes (Rahman et al., 10 Oct 2025). Relative to these, JAMSessions in 5G is a suppressor rather than a detector: it assumes interference directions can be estimated and nulled.

The literature also includes approaches that invert the usual destructive interpretation of jamming. AAJ, or jamming modulation, treats the jammer waveform as a carrier and uses programmable gain to energy-modulate it; its BER decreases with JNR and its capacity exceeds direct transmission when JNR is sufficiently high, with the cited crossover at $\hat{R}_x \leftarrow \hat{R}_x + \delta I$8 for SNR $\hat{R}_x \leftarrow \hat{R}_x + \delta I$9 (Ma et al., 2022). Jam-X replaces acknowledgment packets with intentional jamming pulses for agreement under interference, outperforming packet-based handshakes in agreement probability, energy, and completion time (Boano et al., 2012). “Communication Through Jamming over a Slotted ALOHA Channel” formalizes jamming as an illegitimate signaling medium and derives achievable rates and outer bounds for this hidden channel (0808.0558). These works show that jamming can be exploited as a signaling primitive rather than only mitigated.

A separate line studies structured or targeted attacks. “Jam Sessions: Analysis and Experimental Evaluation of Advanced Jamming Attacks in MIMO Networks” models pilot, barrage, and ACK jamming and derives conditions such as $9.58\ \mathrm{dB}$00 for pilot jamming to dominate barrage jamming per unit energy (Zhang et al., 2019). “Jamming-Resilient Sparse Delay-Doppler NOMA” instead hardens OTFS/NOMA through randomized sparse active sets, unitary precoding, jammed-bin excision, and superincreasing power allocation, reporting no jammer-induced BER floor and at least a $9.58\ \mathrm{dB}$01 BER-ratio improvement against pattern-aware jammers (Kulhandjian et al., 8 Jun 2026). Together with the 5G JAMSessions framework, these papers underscore that modern anti-jamming design is increasingly structured around geometry, sparsity, target selection, and closed-loop adaptation.

Across these domains, the shared lesson is not terminological uniformity but methodological divergence. “JAMSessions” may denote a beamforming stack, a recommendation dataset, or a jam-derived workflow substrate; adjacent research further uses jam sessions literally for live music interaction or treats jamming itself as a communication resource. The term therefore names a research cluster rather than a single canonical object.